diff --git a/docs/source/_static/custom.css b/docs/source/_static/custom.css index 2a71f26e..500327b9 100644 --- a/docs/source/_static/custom.css +++ b/docs/source/_static/custom.css @@ -34,6 +34,14 @@ dd > dl { padding-top: 0px; } +.backrefs { + font-size: 0; +} + +.backrefs:after { + font-size: initial; + content: "\00a0"; +} .components { display: flex; flex-flow: row wrap; diff --git a/docs/source/benchmarking/agriculture/agro.rst b/docs/source/benchmarking/agriculture/agro.rst index 41be71f0..6f6710e8 100644 --- a/docs/source/benchmarking/agriculture/agro.rst +++ b/docs/source/benchmarking/agriculture/agro.rst @@ -25,7 +25,29 @@ Agronomy Ontology (AgrO) ======================================================================================================== -An ontology is a formal representation of a disciplinary domain, representing a semantic standard that can be employed to annotate data where key concepts are defined, as well as the relationships that exist between those concepts (Gruber, 2009). Ontologies provide a common language for different kinds of data to be easily interpretable and interoperable, allowing for easier aggregation and analysis. The Agronomy Ontology (AgrO) provides terms from the agronomy domain that are semantically organized and can facilitate the collection, storage, and use of agronomic data, enabling easy interpretation and reuse of the data by humans and machines alike. To fully understand the implications of varying practices within cropping systems and derive insights, it is often necessary to pull together information from data in different disciplinary domains. For example, data on field management, soil, weather, and crop phenotypes may need to be aggregated to assess performance of a particular crop under different management interventions. However, agronomic data are often collected, described, and stored in inconsistent ways, impeding data comparison, mining, interpretation, and reuse. The use of standards for metadata and data annotation plays a key role in addressing these challenges. While the CG Core Metadata Schema provides a metadata standard to describe agricultural datasets, the Agronomy Ontology enables the description of agronomic data variables using standard terms. +The Agronomy Ontology (AgrO) provides terms from the agronomy domain +that are semantically organized and facilitate the collection, storage, +and use of agronomic data, enabling easier interpretation and reuse by +both humans and machines [#cgiar]_ [#obo]_. To analyze the effects of +varying practices within cropping systems, it is often necessary to +integrate data from multiple disciplinary domains. For example, data on +field management, soil, weather, and crop phenotypes may need to be +combined to assess crop performance under different management +interventions. However, agronomic data are often collected, described, +and stored in inconsistent ways, which impedes data comparison, mining, +interpretation, and reuse [#cgiar]_. The use of standards for metadata +and data annotation plays a key role in addressing these challenges. +While the CG Core Metadata Schema provides a metadata standard to +describe agricultural datasets, the Agronomy Ontology enables the +description of agronomic variables using standardized and semantically +defined terms [#cgiar]_ [#agrofims]_. AgrO specifically covers agronomic +practices, techniques, and variables used in agronomic experiments and +reuses terms from other ontologies to support interoperability [#obo]_. + +**Example Usage**: Annotate agronomic field experiment data with AgrO terms for management +practices, treatments, and measured variables to enable standardized +description, interoperable storage, and cross-study comparison of +agricultural data [#obo]_ [#agrofims]_. Metrics & Statistics -------------------------- @@ -134,3 +156,20 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#cgiar] CGIAR. n.d. "Agronomy Ontology." + Available at: https://bigdata.cgiar.org/resources/agronomy-ontology/ + +.. [#obo] OBO Foundry. n.d. "Agronomy Ontology (AGRO)." + Available at: https://obofoundry.org/ontology/agro.html + +.. [#agrofims] Devare, M., Aubert, C., Benites Alfaro, O. E., + Perez Masias, I. O., and Laporte, M.-A. 2021. + "AgroFIMS: A Tool to Enable Digital Collection of + Standards-Compliant FAIR Data." + *Frontiers in Sustainable Food Systems* 5:726646. + Available at: + https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2021.726646/full diff --git a/docs/source/benchmarking/agriculture/agrovoc.rst b/docs/source/benchmarking/agriculture/agrovoc.rst index 14c252ca..ec4e6e12 100644 --- a/docs/source/benchmarking/agriculture/agrovoc.rst +++ b/docs/source/benchmarking/agriculture/agrovoc.rst @@ -23,9 +23,28 @@ AGROVOC Multilingual Thesaurus (AGROVOC) ======================================================================================================== -AGROVOC is a comprehensive Linked Open Data resource developed and maintained by the Food and Agriculture Organization (FAO) of the United Nations. It provides a structured collection of agricultural concepts, terms, definitions, and relationships that enable unambiguous identification of resources and standardized indexing. As a multilingual thesaurus, AGROVOC supports multiple languages, facilitating access and visibility of agricultural data across domains and languages. The ontology covers diverse agricultural domains including crops, livestock, farm management practices, soil science, and food production. AGROVOC's hierarchical structure enables both broader and narrower term relationships, supporting semantic interoperability and making searches more efficient. The resource is widely used by research institutions, government agencies, and international organizations for data annotation, knowledge organization, and information retrieval. With millions of concept nodes and sophisticated relationship mappings, AGROVOC serves as a critical backbone for agricultural knowledge representation and data integration in the global agricultural community. - -**Example Usage**: Annotate a multilingual agricultural dataset with AGROVOC terms for crops, soil types, and farming practices to enable standardized indexing and cross-language search in international agricultural databases. +AGROVOC is a multilingual thesaurus and Linked Open Data resource +developed and maintained by the Food and Agriculture Organization (FAO) +of the United Nations [#fao-home]_ [#agrovoc-paper]_. It provides a +structured collection of agricultural concepts, terms, definitions, and +relationships that support unambiguous resource identification, +standardized indexing, and more efficient search [#fao-home]_. As a +multilingual knowledge organization system, AGROVOC facilitates access +to agricultural information across domains and languages [#fao-home]_ +[#agrovoc-paper]_. It covers concepts relevant to food, agriculture, +fisheries, forestry, environment, and related domains, and supports +semantic interoperability through hierarchical and associative +relationships as well as links to other vocabularies and datasets +[#fao-home]_ [#linked-dataset]_. With over 41,000 concepts and extensive +multilingual term coverage, AGROVOC is widely used for data annotation, +knowledge organization, and information retrieval in agricultural and +food-related information systems [#fao-dpg]_ [#agrovoc-paper]_. + +**Example Usage**: Annotate a multilingual agricultural dataset with AGROVOC concepts for +crops, soil types, pests, livestock, and farming practices to enable +standardized indexing, semantic interoperability, and cross-language +search across international agricultural databases and repositories +[#fao-home]_ [#agrovoc-paper]_. Metrics & Statistics -------------------------- @@ -134,3 +153,27 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#fao-home] Food and Agriculture Organization of the United Nations + (FAO). n.d. "AGROVOC." Available at: + https://www.fao.org/agrovoc/ + +.. [#agrovoc-paper] Subirats-Coll, I., Kolshus, K., Turbati, A., + Stellato, A., Mietzsch, E., Martini, D., and Zeng, M. 2022. + "AGROVOC: The linked data concept hub for food and agriculture." + *Computers and Electronics in Agriculture* 196:105965. + doi:10.1016/j.compag.2020.105965 + +.. [#linked-dataset] Caracciolo, C., Stellato, A., Morshed, A., + Johannsen, G., Rajbhandari, S., Jaques, Y., and Keizer, J. 2013. + "The AGROVOC Linked Dataset." *Semantic Web* 4(3):341-348. + Available at: + https://www.fao.org/agrovoc/publications/agrovoc-linked-dataset + +.. [#fao-dpg] Food and Agriculture Organization of the United Nations + (FAO). 2024. "AGROVOC is now a certified Digital Public Good!" + Available at: + https://www.fao.org/agora/news/agrovoc-now-certified-digital-public-good diff --git a/docs/source/benchmarking/agriculture/atol.rst b/docs/source/benchmarking/agriculture/atol.rst index 40263d23..360d42ce 100644 --- a/docs/source/benchmarking/agriculture/atol.rst +++ b/docs/source/benchmarking/agriculture/atol.rst @@ -23,14 +23,29 @@ Animal Trait Ontology for Livestock (ATOL) ======================================================================================================== -ATOL (Animal Trait Ontology for Livestock) is an ontology of characteristics defining phenotypes of livestock in their environment. ATOL aims to: provide a reference ontology of phenotypic traits of farm animals for the international scientific and educational communities, farmers, etc.; deliver this reference ontology in a language which can be used by computers in order to support database management, semantic analysis and modeling; represent traits as generic as possible for livestock vertebrates; make the ATOL ontology as operational as possible and closely related to measurement techniques; and structure the ontology in relation to animal production. - -The ontology employs a class-based modeling approach, defining classes for different types of phenotypic traits, measurement techniques, and related data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. ATOL supports the integration of data from various sources, promoting interoperability and data-driven research in animal science. - -Typical applications of ATOL include the development of new phenotypic trait analysis methods, the optimization of livestock management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, ATOL enhances collaboration and innovation in the field of animal science. - -**Example Usage**: -Annotate a livestock dataset with ATOL terms to specify phenotypic traits, measurement techniques, and related data, enabling semantic search and integration with animal science research platforms. +ATOL (Animal Trait Ontology for Livestock) is an ontology of +characteristics defining phenotypes of livestock in their environment +[#inra]_ [#atol-paper]_. ATOL aims to provide a reference ontology of +phenotypic traits for farm animals for the international scientific and +educational communities and other stakeholders, and to deliver this +reference ontology in a form that can be used by computers to support +database management, semantic analysis, and modeling [#inra]_. It is +designed to represent traits as generically as possible for livestock +vertebrates, to remain closely related to measurement techniques, and to +structure the ontology in relation to animal production [#inra]_. The +multi-species ATOL model was developed as a reference source for +indexing phenotype databases and scientific papers, and it covers major +livestock production topics including growth and meat quality, animal +nutrition, milk production, reproduction, and welfare [#atol-paper]_. +By providing a standardized vocabulary and semantic framework, ATOL +supports consistent annotation, interoperability, and integration of +livestock phenotype data across animal science resources [#inra]_ +[#agroportal]_. + +**Example Usage**: Annotate a livestock dataset with ATOL terms to specify phenotypic +traits, measurement techniques, and related data, enabling semantic +search and integration with animal science research platforms +[#inra]_ [#atol-paper]_. Metrics & Statistics -------------------------- @@ -139,3 +154,19 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#inra] INRAE Open Data. n.d. "Animal Trait Ontology for Livestock." + Available at: https://opendata.inra.fr/ATOL/page/ + +.. [#agroportal] AgroPortal. n.d. "ATOL | Summary." + Available at: https://agroportal.lirmm.fr/ontologies/ATOL + +.. [#atol-paper] Golik, W., Dameron, O., Bugeon, J., Fatet, A., Hue, I., + Hurtaud, C., Reichstadt, M., Salaün, M.-C., Vernet, J., Joret, L., + Papazian, F., Nédellec, C., and Le Bail, P.-Y. 2012. + "ATOL: The Multi-species Livestock Trait Ontology." + In *Metadata and Semantics Research*, CCIS 343, 289-300. + doi:10.1007/978-3-642-35233-1_28 diff --git a/docs/source/benchmarking/agriculture/foodon.rst b/docs/source/benchmarking/agriculture/foodon.rst index 683fe0c5..4b71360e 100644 --- a/docs/source/benchmarking/agriculture/foodon.rst +++ b/docs/source/benchmarking/agriculture/foodon.rst @@ -25,7 +25,31 @@ Food Ontology (FoodON) ======================================================================================================== -FoodOn, the Food Ontology, provides comprehensive vocabulary for naming and classifying food materials throughout the entire food supply chain. It encompasses raw harvested foods and their botanical/zoological origins as well as processed food products designed for both human consumption and animal feed. FoodOn integrates anatomical and taxonomic knowledge, enabling precise semantic representation of food items and their components. The ontology is designed as a neutral, ontology-driven standard that bridges the interests of government agencies, industry stakeholders, nonprofits, and consumers. Its hierarchical structure captures relationships between ingredient components, processing methods, and final food products. FoodOn facilitates standardized naming conventions and interoperability across diverse food-related databases and systems. The ontology supports critical applications including food safety traceability, nutritional analysis, dietary research, and supply chain transparency. By providing unambiguous semantic definitions, FoodOn enables automated systems to track food products, ingredients, and allergens, enhancing data integration across the food industry and supporting evidence-based policy decisions in food security and nutrition. +FoodOn is a farm-to-fork food ontology that provides a comprehensive +vocabulary for naming and classifying food materials across the food +supply chain [#foodon-home]_ [#foodon-paper]_. It covers raw harvested +foods, their botanical and zoological origins, and processed food +products intended for both human consumption and animal feed +[#foodon-home]_ [#foodon-paper]_. FoodOn integrates anatomical, +taxonomic, and other reusable ontology terms to support precise semantic +representation of food items, their components, and related food +processes [#foodon-home]_ [#foodon-paper]_. It is designed as an open, +ontology-driven standard that supports consistent food description and +interoperability across government, industry, research, and other +food-related systems [#foodon-paper]_ [#foodon-home]_. Its hierarchical +structure supports relationships among source organisms, anatomical +parts, processing methods, preservation methods, packaging, and final +food products [#foodon-home]_ [#foodon-paper]_. By providing +unambiguous semantic definitions, FoodOn supports standardized naming, +food safety traceability, quality control, nutrition and dietary +research, and data integration across food-related databases and +applications [#foodon-paper]_ [#foodon-home]_. + +**Example Usage**: Annotate a food product dataset with FoodOn terms for source organisms, +anatomical parts, processing methods, packaging, and final food products +to enable standardized description, interoperable data exchange, and +traceability across food safety, nutrition, and supply chain systems +[#foodon-home]_ [#foodon-paper]_. Metrics & Statistics -------------------------- @@ -134,3 +158,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#foodon-home] FoodOn. n.d. "FoodOn: A farm to fork ontology." + Available at: https://foodon.org/ + +.. [#foodon-paper] Dooley, D. M., Griffiths, E. J., Gosal, G. S., + Buttigieg, P. L., Hoehndorf, R., Lange, M. C., Schriml, L. M., + Brinkman, F. S. L., and Hsiao, W. W. L. 2018. + "FoodOn: a harmonized food ontology to increase global food + traceability, quality control and data integration." + *npj Science of Food* 2:23. + doi:10.1038/s41538-018-0032-6 diff --git a/docs/source/benchmarking/agriculture/po.rst b/docs/source/benchmarking/agriculture/po.rst index d42d6e22..621c770d 100644 --- a/docs/source/benchmarking/agriculture/po.rst +++ b/docs/source/benchmarking/agriculture/po.rst @@ -25,7 +25,30 @@ Plant Ontology (PO) ======================================================================================================== -The Plant Ontology (PO) is a structured vocabulary and database resource that links plant anatomy, morphology and growth and development to plant genomics data. Developed collaboratively by plant biologists and ontology experts, PO provides a comprehensive framework for describing plant structures and developmental stages. The ontology integrates anatomical terms that can be associated with plant genes, enabling researchers to annotate phenotypic data and support comparative genomics across plant species. PO is designed to facilitate seamless data integration and interoperability in plant science research, allowing scientists to discover relationships between genes and plant structures. With its hierarchical organization of plant parts from whole organism level down to cellular structures, PO supports diverse applications including literature curation, genome annotation, and systems biology studies. The ontology is actively maintained by the Planteome project and integrated with other biological ontologies to ensure compatibility and comprehensive semantic representation in the plant science community. +The Plant Ontology (PO) is a structured vocabulary and ontology +resource that links plant anatomy, morphology, growth, and development +to plant genomics and phenomics data [#obo]_ [#po-paper]_. Developed as +a community resource, PO provides a framework for describing plant +structures and developmental stages across plant species [#obo]_ +[#po-dev-paper]_. The ontology integrates anatomical and developmental +terms that can be associated with plant genes and phenotypes, enabling +researchers to annotate data and support comparative genomics and +comparative plant biology [#po-paper]_ [#po-dev-paper]_. PO is designed +to facilitate data integration and interoperability in plant science +research [#obo]_ [#po-paper]_. With its hierarchical organization of +plant structures and developmental stages, including whole plants, +organs, tissues, and cell types, PO supports applications such as +literature curation, genome annotation, and phenotypic data annotation +[#po-paper]_. The ontology is under active development and is integrated +with the Planteome project and other biological ontologies to support +semantic compatibility in the plant science community [#obo]_ +[#planteome]_. + +**Example Usage**: Annotate a plant genomics or phenomics dataset with PO terms for plant +structures and developmental stages, such as leaf, root, flower, seed, +or senescent stage, to enable standardized annotation, cross-species +comparison, and integration with plant science databases and analysis +platforms [#obo]_ [#po-paper]_. Metrics & Statistics -------------------------- @@ -134,3 +157,32 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#obo] OBO Foundry. n.d. "Plant Ontology (PO)." + Available at: `https://obofoundry.org/ontology/po.html `_ + +.. [#po-paper] Cooper, L., Walls, R. L., Elser, J., Gandolfo, M. A., + Stevenson, D. W., Smith, B., Preece, J., Athreya, B., Mungall, C. J., + and Rensing, S. A. 2013. "The Plant Ontology as a Tool for Comparative + Plant Anatomy and Genomic Analyses." + *Plant and Cell Physiology* 54(2): e1. + doi:10.1093/pcp/pcs163 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC3583023/ `_ + +.. [#po-dev-paper] Walls, R. L., Cooper, L., Elser, J., Gandolfo, M. A., + Mungall, C. J., Smith, B., Stevenson, D. W., and Jaiswal, P. 2019. + "The Plant Ontology Facilitates Comparisons of Plant Development + Stages Across Species." + *Frontiers in Plant Science* 10:631. + doi:10.3389/fpls.2019.00631 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC6558174/ `_ + +.. [#planteome] Cooper, L., Elser, J., Laporte, M.-A., Arnaud, E., + and Jaiswal, P. 2024. "Planteome 2024 Update: Reference Ontologies + and Knowledgebase for Plant Biology." + *Nucleic Acids Research* 52(D1): D1548-D1555. + doi:10.1093/nar/gkad1028 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC10767901/ `_ diff --git a/docs/source/benchmarking/arts_and_humanities/chordontology.rst b/docs/source/benchmarking/arts_and_humanities/chordontology.rst index a5e0c7f8..1ad1bb18 100644 --- a/docs/source/benchmarking/arts_and_humanities/chordontology.rst +++ b/docs/source/benchmarking/arts_and_humanities/chordontology.rst @@ -25,7 +25,28 @@ Chord Ontology (ChordOntology) ======================================================================================================== -The Chord Ontology is a formal representation for describing and classifying chords in musical compositions. It provides a structured vocabulary for representing harmonic concepts and chord structures, enabling precise annotation and analysis of music at the semantic level. The ontology captures essential chord properties including chord type (major, minor, diminished, augmented), root note, and constituent pitch classes. It facilitates semantic annotation of audio files, musical scores, and music information retrieval systems, allowing researchers and musicians to query and discover musical pieces based on harmonic content. The ontology integrates with broader music theory frameworks and supports interoperability with other music-related ontologies. By formalizing chord relationships and structures, the Chord Ontology enables computational music analysis, music recommendation systems, and digital musicology applications. It provides a common framework for music annotation across diverse platforms and datasets, supporting music education, composition analysis, and music information systems development. +The Chord Ontology is a formal representation for describing and classifying +chords and chord sequences in musical resources. It provides a structured +vocabulary for representing harmonic concepts and chord structures, enabling +semantic annotation and analysis of music data. The ontology captures core +chord properties including chord type (for example major, minor, diminished, +and augmented), root note, constituent intervals, and bass note. It supports +the annotation of audio files, musical scores, and symbolic music files by +linking chord events to temporal structures and music resources. The ontology +was developed within the OMRAS2 project and is designed to interoperate with +related Semantic Web resources such as the Music Ontology, Timeline Ontology, +and Event Ontology. By formalizing chord relationships and structures, the +Chord Ontology supports computational music analysis, harmonic annotation, +music information retrieval, and digital musicology applications. It provides +a common framework for music annotation across datasets and tools, supporting +harmonic analysis, corpus annotation, and music information systems +development [#chord-spec]_ [#omras2]_ [#music-ontology]_. + +**Example Usage**: Annotate the harmonic timeline of an audio recording, +musical score, or symbolic music file with Chord Ontology terms for chord +events, root notes, intervals, and bass notes to enable semantic search, +computational harmonic analysis, and integration with music information +retrieval datasets and tools [#chord-spec]_ [#omras2]_. Metrics & Statistics -------------------------- @@ -134,3 +155,24 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#chord-spec] Sutton, C., Raimond, Y., and Mauch, M. 2007. + "Chord Ontology Specification." + OMRAS2 Project, Centre for Digital Music, Queen Mary University of London. + Available at: http://purl.org/ontology/chord/ + Also available at: https://motools.sourceforge.net/chord_draft_1/chord.html + +.. [#omras2] Fazekas, G., Raimond, Y., Jacobson, K., and Sandler, M. 2010. + "An Overview of Semantic Web Activities in the OMRAS2 Project." + *Journal of New Music Research* 39(4): 295-311. + doi:10.1080/09298215.2010.536555 + +.. [#music-ontology] Raimond, Y., Abdallah, S. A., Sandler, M. B., + and Giasson, F. 2007. + "The Music Ontology." + In *Proceedings of the 8th International Conference on Music Information + Retrieval (ISMIR 2007)*, Vienna, Austria, pp. 417-422. + Available at: https://ismir2007.ismir.net/proceedings/ISMIR2007_p417_raimond.pdf diff --git a/docs/source/benchmarking/arts_and_humanities/icon.rst b/docs/source/benchmarking/arts_and_humanities/icon.rst index 96b82c9b..fcdb9b9b 100644 --- a/docs/source/benchmarking/arts_and_humanities/icon.rst +++ b/docs/source/benchmarking/arts_and_humanities/icon.rst @@ -25,7 +25,33 @@ Icon Ontology (ICON) ======================================================================================================== -The ICON ontology provides a formal framework for high-granularity art interpretation and analysis. It was developed by conceptualizing Panofsky's theory of levels of interpretation, enabling artworks to be systematically described according to three complementary analytical perspectives: Pre-iconographical (visual elements and their formal properties), Iconographical (symbolic meaning and subject matter), and Iconological (deeper cultural and philosophical context). This three-level approach enables comprehensive semantic annotation of artworks, capturing both surface-level visual descriptions and deeper interpretive insights. The ontology supports structured knowledge representation of artistic elements, iconographic themes, cultural references, and symbolic meanings. It facilitates semantic interoperability in digital art collections, museum databases, and art history research platforms. The ICON ontology enables advanced search and discovery capabilities based on artistic interpretation levels, supporting art historians, curators, and researchers in analyzing and understanding artworks. It also supports linked data integration with other cultural heritage ontologies and knowledge bases, enabling rich cross-domain art historical research and interpretation analysis. +The ICON ontology provides a formal framework for high-granularity art +interpretation and analysis. It was developed by conceptualizing Panofsky’s +theory of levels of interpretation, enabling artworks to be systematically +described according to three complementary analytical perspectives: +Pre-iconographical (visual recognition and formal elements), Iconographical +(subject matter and represented themes), and Iconological (deeper symbolic, +cultural, and philosophical meanings) [#icon-paper]_. This three-level +approach enables comprehensive semantic annotation of artworks, capturing +both surface-level visual descriptions and deeper interpretive insights +[#icon-paper]_ [#icon-extension]_. The ontology supports structured +knowledge representation of artistic elements, iconographic themes, +cultural references, and symbolic meanings [#icon-paper]_. It facilitates +semantic interoperability in linked cultural heritage data by aligning with +other ontologies and knowledge bases, including CIDOC-CRM and VIR +[#icon-paper]_ [#icon-doc]_. The ICON ontology enables detailed modelling +of artistic interpretation for art history research, corpus annotation, +and linked data applications, supporting researchers and curators in +analyzing and comparing artworks across interpretation levels +[#icon-paper]_ [#icon-extension]_. + +**Example Usage**: Annotate an artwork or museum object with ICON ontology +terms describing pre-iconographical elements, iconographical subjects, +and iconological meanings for example, identifying depicted figures, +their symbolic attributes, and the broader cultural or philosophical +interpretation of the scene to support semantic search, comparative art +historical analysis, and linked data integration across cultural heritage +datasets [#icon-paper]_ [#icon-extension]_. Metrics & Statistics -------------------------- @@ -134,3 +160,24 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#icon-paper] Sartini, B., Baroncini, S., van Erp, M., Tomasi, F., + and Gangemi, A. 2023. "ICON: An Ontology for Comprehensive Artistic + Interpretations." + *ACM Journal on Computing and Cultural Heritage* 16(3), Article 59: 1-38. + doi:10.1145/3594724 + +.. [#icon-extension] Sartini, B. 2023. + "A Comparative Study of Simple and Complex Art Interpretations in + Linked Open Data Using ICON Ontology." + In *Proceedings of the Semantic Web and Ontology Design for Cultural + Heritage Workshop (SWODCH 2023)*. + CEUR Workshop Proceedings 3540. + Available at: `https://ceur-ws.org/Vol-3540/paper4.pdf `_ + +.. [#icon-doc] ICON Ontology Documentation. n.d. + "ICON Ontology Documentation 2.0." + Available at: `https://br0ast.github.io/ICON/ICONOntologyDocumentation2.0/index-en.html `_ diff --git a/docs/source/benchmarking/arts_and_humanities/musicontology.rst b/docs/source/benchmarking/arts_and_humanities/musicontology.rst index 587e007d..788c7432 100644 --- a/docs/source/benchmarking/arts_and_humanities/musicontology.rst +++ b/docs/source/benchmarking/arts_and_humanities/musicontology.rst @@ -26,6 +26,32 @@ Music Ontology (MusicOntology) ======================================================================================================== The Music Ontology Specification provides a comprehensive framework for describing music and related entities on the Semantic Web. It defines core concepts and properties for representing artists, albums, tracks, performances, and musical relationships. The ontology enables standardized music metadata annotation, facilitating interoperability across music information systems, streaming platforms, and digital libraries. It supports rich description of musical works including production details, distribution information, and artistic collaborations. The Music Ontology integrates with other semantic web vocabularies and allows linking of music resources with external datasets and knowledge bases. It enables music recommendation systems, search engines, and music discovery applications to leverage structured semantic data. The ontology supports various music-related use cases including discography management, performance tracking, playlist creation, and music history documentation. By providing a common framework for music representation, the Music Ontology facilitates semantic data integration across the music industry and enables advanced music information retrieval and analysis capabilities. +The Music Ontology Specification provides a comprehensive framework for +describing music and related entities on the Semantic Web. It defines +core concepts and properties for representing artists, albums, tracks, +performances, recordings, and musical relationships [#mo-spec]_ +[#mo-paper]_. The ontology enables standardized music metadata +annotation, facilitating interoperability across music information +systems and digital music libraries [#mo-spec]_ [#mo-paper]_. It +supports rich description of musical works, performances, recordings, +signals, and associated editorial, cultural, and acoustic information +[#mo-paper]_ [#mo-spec]_. The Music Ontology integrates with other +Semantic Web vocabularies and allows linking music resources with +external datasets and knowledge bases [#mo-paper]_. It provides a common +framework for publishing and integrating structured music-related data, +supporting applications such as discography representation, performance +description, playlist and collection modelling, and music information +retrieval [#mo-spec]_ [#mo-paper]_. By enabling interoperable semantic +descriptions of music resources, the Music Ontology supports data +integration and analysis across diverse music datasets and tools +[#mo-paper]_. + +**Example Usage**: Annotate a music dataset, recording collection, or +linked music catalog with Music Ontology terms for artists, albums, +tracks, performances, recordings, and release relationships to enable +semantic search, metadata integration, and interoperability with music +information retrieval and linked data applications [#mo-spec]_ +[#mo-paper]_. Metrics & Statistics -------------------------- @@ -134,3 +160,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#mo-spec] Raimond, Y., Abdallah, S. A., Sandler, M. B., + and Giasson, F. n.d. "Music Ontology Specification." + Available at: `https://motools.sourceforge.net/doc/musicontology.html `_ + +.. [#mo-paper] Raimond, Y., Abdallah, S. A., Sandler, M. B., + and Giasson, F. 2007. "The Music Ontology." + In *Proceedings of the 8th International Conference on Music + Information Retrieval (ISMIR 2007)*, Vienna, Austria, + pp. 417-422. + Available at: `https://ismir2007.ismir.net/proceedings/ISMIR2007_p417_raimond.pdf `_ diff --git a/docs/source/benchmarking/arts_and_humanities/nomisma.rst b/docs/source/benchmarking/arts_and_humanities/nomisma.rst index 0e99de67..4b94637f 100644 --- a/docs/source/benchmarking/arts_and_humanities/nomisma.rst +++ b/docs/source/benchmarking/arts_and_humanities/nomisma.rst @@ -25,7 +25,31 @@ Nomisma Ontology (Nomisma) ======================================================================================================== -The Nomisma Ontology is a collaborative project that provides stable, standardized digital representations of numismatic concepts following the principles of Linked Open Data. It offers HTTP URIs that provide persistent access to information about numismatic entities in multiple formats, enabling seamless integration with other linked open data resources. Developed collaboratively by the American Numismatic Society and the Institute for the Study of the Ancient World at New York University, Nomisma represents a comprehensive framework for describing coins, denominations, mints, rulers, and monetary systems across different periods and cultures. The ontology facilitates semantic annotation of numismatic data, supporting interoperability across digital coin collections, archaeological databases, and historical research platforms. It enables advanced querying and analysis capabilities for numismatists, archaeologists, and historians seeking to understand monetary systems and economic aspects of historical societies. The ontology integrates with broader cultural heritage and historical linked data ecosystems, supporting cross-domain research on ancient economies, trade networks, and political history. By providing standardized semantic representations, Nomisma enhances discoverability and reusability of numismatic data in the global digital humanities and cultural heritage communities. +The Nomisma Ontology is a collaborative framework that provides stable, +standardized digital representations of numismatic concepts according to +the principles of Linked Open Data. It offers HTTP URIs that provide +persistent access to reusable information about numismatic entities and +related concepts, enabling integration with other linked data resources +[#nomisma-project]_ [#numismatics-lod]_. The Nomisma community maintains +a formalized RDF ontology and a data model for encoding concepts, coins, +typologies, hoards, and other kinds of numismatic objects as linked open +data [#nomisma-project]_. The ontology and controlled vocabulary support +the description of entities such as coins, denominations, mints, rulers, +regions, and related numismatic concepts across different periods and +cultures [#nomisma-project]_ [#diginuma]_. It facilitates semantic +annotation of numismatic data and supports interoperability across +digital coin collections, archaeological datasets, and historical +research resources [#numismatics-lod]_ [#diginuma]_. By providing +standardized semantic representations, Nomisma enables querying, +integration, and comparative analysis of monetary and material culture +data within broader cultural heritage and digital humanities ecosystems +[#numismatics-lod]_ [#semantic-solutions]_. + +**Example Usage**: Annotate a digital coin collection, hoard dataset, or +archaeological numismatic catalog with Nomisma terms for coin types, +denominations, mints, rulers, regions, and materials to enable semantic +search, linked data integration, and comparative analysis across +numismatic and historical datasets [#nomisma-project]_ [#numismatics-lod]_. Metrics & Statistics -------------------------- @@ -134,3 +158,26 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#nomisma-project] Deutsches Archäologisches Institut. n.d. + "Nomisma.org - a linked open data approach to numismatics." + Available at: `https://www.dainst.org/en/research/projects/nomismaorg-a-linked-data-approach-to-numismatics/2098 `_ + +.. [#numismatics-lod] Gruber, E. 2021. "Numismatics and Linked Open Data." + *ISAW Papers* 20(6). + Available at: `https://dlib.nyu.edu/awdl/isaw/isaw-papers/20-6/ `_ + +.. [#diginuma] Rantala, H., Oksanen, E., Ehrnsten, F., and Hyvönen, E. 2022. + "Harmonizing and Using Numismatic Linked Data in Digital Humanities + Research and Application Development: Case DigiNUMA." + In *The Semantic Web: ESWC 2022 Satellite Events*, pp. 141-146. + Available at: `https://2022.eswc-conferences.org/wp-content/uploads/2022/05/pd_Rantala_et_al_paper_238.pdf `_ + +.. [#semantic-solutions] Hyvönen, E., Rantala, H., Oksanen, E., and + Ehrnsten, F. 2023. "Semantic Solutions for Democratizing + Archaeological and Numismatic Data." + *Journal on Computing and Cultural Heritage* 16(4). + doi:10.1145/3625302 diff --git a/docs/source/benchmarking/arts_and_humanities/timelineontology.rst b/docs/source/benchmarking/arts_and_humanities/timelineontology.rst index 61f4378b..3af397a7 100644 --- a/docs/source/benchmarking/arts_and_humanities/timelineontology.rst +++ b/docs/source/benchmarking/arts_and_humanities/timelineontology.rst @@ -25,7 +25,33 @@ Timeline Ontology (TimelineOntology) ======================================================================================================== -The Timeline Ontology provides a formal framework for representing and managing temporal information in multimedia and music contexts. It centers around the notion of timelines as temporal backbones that can support various types of media including signals, videos, musical scores, and musical works. The ontology enables precise temporal annotation and synchronization of multimedia elements, allowing for structured representation of time-based relationships between different media components. It supports rich temporal modeling including durations, intervals, and temporal landmarks within multimedia documents. The Timeline Ontology facilitates content synchronization across different representations, such as aligning audio signals with musical notation or video with accompanying metadata. It is particularly valuable for music information retrieval systems, multimedia annotation tools, and digital humanities research. The ontology enables advanced applications including temporal querying of multimedia content, cross-media alignment, and time-aware metadata management. By providing a common temporal framework, the Timeline Ontology supports interoperability in music and media analysis systems, enabling researchers and practitioners to work with complex temporal structures in a standardized, machine-readable format. +The Timeline Ontology provides a formal framework for representing and +managing temporal information in multimedia and music contexts. It is +centered around the notion of timelines as temporal backbones that can +support various types of media and temporal objects, including signals, +videos, performances, scores, and musical works [#timeline-spec]_. The +ontology enables precise temporal annotation by allowing instants and +intervals to be defined on a timeline, supporting structured +representation of time-based relationships between different media +components [#timeline-spec]_. It supports temporal modelling of +durations, intervals, and temporal positions within multimedia and music +resources [#timeline-spec]_ [#mo-paper]_. The Timeline Ontology +facilitates synchronization across different representations, such as +aligning audio signals with musical notation or linking performances and +recordings to temporal metadata [#timeline-spec]_ [#mo-paper]_. It is +particularly useful in music information retrieval, multimedia +annotation, and Semantic Web applications that require machine-readable +temporal descriptions [#timeline-spec]_ [#omras2]_. By providing a +common temporal framework, the Timeline Ontology supports interoperability +across music and media analysis systems and enables temporal querying and +integration of complex time-based data [#timeline-spec]_ [#omras2]_. + +**Example Usage**: Annotate an audio recording, video, or symbolic music +file with Timeline Ontology terms for timelines, instants, and +intervals in order to align chord events, note events, subtitles, or +performance segments with precise temporal positions, enabling temporal +querying, cross-media synchronization, and interoperable multimedia +annotation [#timeline-spec]_ [#omras2]_. Metrics & Statistics -------------------------- @@ -134,3 +160,23 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#timeline-spec] Raimond, Y., and Abdallah, S. A. 2006. + "The Timeline Ontology." + OWL ontology specification. + Available at: `https://motools.sourceforge.net/timeline/timeline.html `_ + +.. [#mo-paper] Raimond, Y., Abdallah, S. A., Sandler, M. B., + and Giasson, F. 2007. "The Music Ontology." + In *Proceedings of the 8th International Conference on Music + Information Retrieval (ISMIR 2007)*, Vienna, Austria, + pp. 417-422. + Available at: `https://ismir2007.ismir.net/proceedings/ISMIR2007_p417_raimond.pdf `_ + +.. [#omras2] Fazekas, G., Raimond, Y., Jacobson, K., and Sandler, M. 2010. + "An Overview of Semantic Web Activities in the OMRAS2 Project." + *Journal of New Music Research* 39(4): 295-311. + doi:10.1080/09298215.2010.536555 diff --git a/docs/source/benchmarking/biology_and_life_sciences/biopax.rst b/docs/source/benchmarking/biology_and_life_sciences/biopax.rst index 4aac998b..a19032e0 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/biopax.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/biopax.rst @@ -25,9 +25,31 @@ Biological Pathways Exchange (BioPAX) ======================================================================================================== -BioPAX (Biological Pathways Exchange) is a standard RDF/OWL-based language and ontology for exchanging, integrating, and analyzing biological pathway data. It enables comprehensive representation of molecular interaction networks, including biochemical reactions, gene regulatory pathways, signaling cascades, and transport processes. BioPAX models core pathway concepts such as Interactions (reactions, complexes), Participants (proteins, small molecules), Pathways (sequences of interactions), and their physical and functional properties. The ontology is designed to reduce complexity in data interchange by providing a unified format that bridges disparate pathway databases, modeling tools, and computational analysis platforms. It supports interoperability across systems like Reactome, KEGG, and other pathway databases, facilitating systems biology analysis and network visualization. - -**Example Usage**: Represent a phosphorylation reaction as a BioPAX BiochemicalReaction with specific proteins as catalysts and substrates, linked to cellular locations and regulatory conditions. +BioPAX (Biological Pathway Exchange) is a standard RDF/OWL-based +language and ontology for exchanging, integrating, and analyzing +biological pathway data [#biopax-paper]_ [#biopax-spec]_. It enables the +representation of molecular interaction networks, including metabolic and +signaling pathways, molecular and genetic interactions, and gene +regulation processes [#biopax-paper]_ [#biopax-spec]_. BioPAX models +core pathway concepts such as interactions, physical entities +(for example proteins, DNA, RNA, complexes, and small molecules), +pathways, and their associated biological and cellular properties +[#biopax-paper]_ [#biopax-spec]_. The ontology is designed to reduce +complexity in data interchange by providing a unified format that +supports integration across pathway databases, visualization tools, and +computational analysis platforms [#biopax-paper]_. BioPAX is widely used +in pathway informatics and has been adopted by major resources and tools +for pathway data sharing and integration [#biopax-paper]_ [#pathway-commons]_. +By providing a common semantic framework for pathway representation, +BioPAX supports systems biology analysis, pathway visualization, and +interoperable exchange of biological knowledge across diverse resources +[#biopax-paper]_. + +**Example Usage**: Represent a phosphorylation event as a BioPAX +BiochemicalReaction in which a protein substrate is converted into its +phosphorylated form, linked to the relevant catalyst or controller, +cellular location, and pathway context to enable pathway exchange, +visualization, and computational analysis [#biopax-spec]_ [#biopax-paper]_. Metrics & Statistics -------------------------- @@ -136,3 +158,42 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#biopax-paper] Demir, E., Cary, M. P., Paley, S., Fukuda, K., + Lemer, C., Vastrik, I., Wu, G., D'Eustachio, P., Schaefer, C., + Luciano, J., Schacherer, F., Martinez-Flores, I., Hu, Z., + Jimenez-Jacinto, V., Joshi-Tope, G., Kandasamy, K., Lopez-Fuentes, + A. C., Mi, H., Pichler, E., Rodchenkov, I., Splendiani, A., + Tkachev, S., Zucker, J., Gopinath, G., Rajasimha, H., Ramakrishnan, + R., Shah, I., Syed, M., Anwar, N., Babur, O., Blinov, M., Brauner, + E., Corwin, D., Donaldson, S., Gibbons, F., Goldberg, R., + Hornbeck, P., Luna, A., Murray-Rust, P., Neumann, E., Reubenacker, + O., Samwald, M., van Iersel, M., Wimalaratne, S., Allen, K., + Braun, B., Whirl-Carrillo, M., Cheung, K.-H., Dahlquist, K., + Finney, A., Gillespie, M., Glass, E., Gong, L., Haw, R., + Honig, M., Hubaut, O., Kane, D., Krupa, S., Kutmon, M., + Leonard, J., Marks, D., Merberg, D., Petri, V., Pico, A., + Ravenscroft, D., Ren, L., Shah, N., Sunshine, M., Tang, R., + Whaley, R., Letovksy, S., Buetow, K. H., Rzhetsky, A., + Schriml, L. M., Shah, N. H., Wilkinson, M. D., Kelder, T., + Collado-Vides, J., Goto, S., Hofestädt, R., Hermjakob, H., + and Bader, G. D. 2010. "The BioPAX Community Standard for Pathway + Data Sharing." + *Nature Biotechnology* 28(9): 935-942. + doi:10.1038/nbt.1666 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC3001121/ `_ + +.. [#biopax-spec] BioPAX Editorial Board. n.d. + "BioPAX Level 3 Documentation." + Available at: `https://biopax.github.io/Paxtools/ `_ + +.. [#pathway-commons] Cerami, E. G., Gross, B. E., Demir, E., + Rodchenkov, I., Babur, O., Anwar, N., Schultz, N., Bader, G. D., + and Sander, C. 2011. "Pathway Commons, a Web Resource for Biological + Pathway Data." + *Nucleic Acids Research* 39(Database issue): D685-D690. + doi:10.1093/nar/gkq1039 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC3013641/ `_ diff --git a/docs/source/benchmarking/biology_and_life_sciences/efo.rst b/docs/source/benchmarking/biology_and_life_sciences/efo.rst index 80d3f206..87ceb984 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/efo.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/efo.rst @@ -23,10 +23,31 @@ Experimental Factor Ontology (EFO) ======================================================================================================== -The Experimental Factor Ontology (EFO) is a comprehensive ontology developed to provide systematic, standardized descriptions of experimental variables and factors in biological and biomedical research. EFO integrates terms from multiple biological ontologies, including UBERON (anatomy), ChEBI (chemical compounds), and Cell Ontology, to support the annotation, analysis, and visualization of experimental data. It is widely used for annotating datasets in EBI databases, the GWAS catalog, and as the core ontology for Open Targets. EFO enables semantic interoperability, data integration, and advanced queries across diverse experimental datasets, facilitating reproducibility and meta-analysis. The ontology is actively maintained by the EMBL-EBI Samples, Phenotypes and Ontologies Team (SPOT) and is continuously updated to reflect new experimental techniques and research needs. By providing a unified framework for describing experimental factors, EFO supports data sharing, discovery, and knowledge integration in genomics, transcriptomics, and other life sciences domains. - -**Example Usage**: -Annotate a gene expression dataset with EFO terms to specify experimental variables such as tissue type, disease state, treatment, and assay platform, enabling cross-study comparison and meta-analysis. +The Experimental Factor Ontology (EFO) is a comprehensive ontology +developed to provide systematic, standardized descriptions of +experimental variables and factors in biological and biomedical research +[#efo-site]_ [#efo-faq]_. EFO integrates terms from multiple biological +ontologies, including UBERON for anatomy, ChEBI for chemical compounds, +and the Cell Ontology, in order to support the annotation, analysis, +and visualization of experimental data [#efo-site]_ [#efo-faq]_. It is +widely used for annotating datasets in EMBL-EBI resources and external +projects such as the NHGRI-EBI GWAS Catalog, and it is also used as the +core ontology for Open Targets [#efo-site]_ [#gwas-2023]_. EFO enables +semantic interoperability, data integration, and ontology-based querying +across diverse datasets, facilitating cross-study comparison and data +reuse [#efo-site]_ [#gwas-2018]_. The ontology is actively maintained at +EMBL-EBI and continues to evolve in response to new data types and +research needs [#efo-team]_ [#efo-site]_. By providing a unified +framework for describing experimental factors, EFO supports data +sharing, discovery, and knowledge integration in genomics, +transcriptomics, and related life science domains [#efo-site]_ +[#gwas-2023]_. + +**Example Usage**: Annotate a gene expression or association dataset with +EFO terms to specify experimental variables such as tissue type, +disease or phenotype, treatment, and assay-related factors, enabling +semantic search, cross-study comparison, and meta-analysis across +biological datasets [#efo-site]_ [#gwas-2018]_. Metrics & Statistics -------------------------- @@ -135,3 +156,37 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#efo-site] EMBL-EBI. n.d. "The Experimental Factor Ontology." + Available at: `https://www.ebi.ac.uk/efo/ `_ + +.. [#efo-faq] EMBL-EBI. n.d. "FAQ EFO." + Available at: `https://www.ebi.ac.uk/efo/faq.html `_ + +.. [#efo-team] EMBL-EBI. n.d. "Samples, Phenotypes and Ontologies." + Available at: `https://www.ebi.ac.uk/about/teams/samples-phenotypes-ontologies/ `_ + +.. [#gwas-2018] Buniello, A., MacArthur, J. A. L., Cerezo, M., + Harris, L. W., Hayhurst, J., Malangone, C., McMahon, A., + Morales, J., Mountjoy, E., Sollis, E., Suveges, D., Vrousgou, O., + Whetzel, P. L., Amode, R., Guillen, J. A., Riat, H. S., + Trevanion, S. J., Hall, P., Junkins, H., Flicek, P., + Burdett, T., Hindorff, L. A., Cunningham, F., and Parkinson, H. + 2019. "The NHGRI-EBI GWAS Catalog of Published Genome-Wide + Association Studies, Targeted Arrays and Summary Statistics 2019." + *Nucleic Acids Research* 47(D1): D1005-D1012. + doi:10.1093/nar/gky1120 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC6323933/ `_ + +.. [#gwas-2023] Sollis, E., Mosaku, A., Abid, A., Buniello, A., + Cerezo, M., Gil, L., Groza, T., Güneş, O., Hall, P., + Hayhurst, J. D., McMahon, A., Mountjoy, E., Parton, A., + Paschall, J., Lopes, E. N., Sanseau, P., Shamout, S., + Sheth, T., Riat, H. S., et al. 2023. "NHGRI-EBI GWAS Catalog: + Knowledgebase and Deposition Resource." + *Nucleic Acids Research* 51(D1): D977-D985. + doi:10.1093/nar/gkac1010 + Available at: `https://academic.oup.com/nar/article/51/D1/D977/6814460 `_ diff --git a/docs/source/benchmarking/biology_and_life_sciences/go.rst b/docs/source/benchmarking/biology_and_life_sciences/go.rst index bfdf1929..79e31d71 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/go.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/go.rst @@ -25,9 +25,31 @@ Gene Ontology (GO) ======================================================================================================== -The Gene Ontology (GO) is a comprehensive resource that provides structured controlled vocabularies for the annotation of gene products with respect to their molecular function, cellular component, and biological process role. Developed collaboratively by a consortium of model organism databases and bioinformatics resources, GO enables consistent annotation of genes and proteins across diverse species and databases. The ontology is organized into three hierarchical namespaces: Biological Process (GO:BP) describing what the gene product does in biological context, Molecular Function (GO:MF) characterizing its biochemical activity, and Cellular Component (GO:CC) indicating where it functions. GO supports sophisticated biological data analysis, enabling researchers to discover functional similarities between genes, identify enriched biological processes in genomics datasets, and understand relationships between genes in biological systems. - -**Example Usage**: Annotate a protein like "TP53" with GO terms such as "GO:0006294 (nucleotide-excision repair)" for process, "GO:0003677 (DNA binding)" for molecular function, and "GO:0005634 (nucleus)" for cellular component. +The Gene Ontology (GO) is a comprehensive resource that provides +structured controlled vocabularies for the annotation of gene products +with respect to their molecular function, cellular component, and +biological process roles [#go-site]_ [#go-paper]_. Developed +collaboratively by the Gene Ontology Consortium, GO enables consistent +annotation of genes and proteins across diverse species and databases +[#go-paper]_ [#go-2026]_. The ontology is organized into three +hierarchical namespaces: Biological Process (BP), describing the larger +biological objectives to which a gene product contributes; Molecular +Function (MF), characterizing its molecular activity; and Cellular +Component (CC), indicating where that activity occurs [#go-overview]_ +[#go-annotations]_. GO supports biological data analysis by enabling +researchers to compare gene functions, identify enriched biological +processes or functions in genomics datasets, and understand +relationships among genes and gene products in biological systems +[#go-paper]_ [#go-2026]_. By providing a shared semantic framework for +functional annotation, GO facilitates data integration, comparative +genomics, and computational analysis across the life sciences +[#go-paper]_ [#go-2026]_. + +**Example Usage**: Annotate a protein such as TP53 with GO terms for +biological process, molecular function, and cellular component. For +example, terms related to apoptotic process, DNA binding, and nucleus to +enable standardized functional annotation, enrichment analysis, and +cross-database comparison [#go-annotations]_ [#go-site]_. Metrics & Statistics -------------------------- @@ -136,3 +158,28 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#go-site] Gene Ontology Consortium. n.d. "The Gene Ontology Resource." + Available at: `https://geneontology.org/ `_ + +.. [#go-overview] Gene Ontology Consortium. n.d. "Gene Ontology Overview." + Available at: `https://geneontology.org/docs/ontology-documentation/ `_ + +.. [#go-annotations] Gene Ontology Consortium. n.d. + "Introduction to GO Annotations." + Available at: `https://geneontology.org/docs/go-annotations/ `_ + +.. [#go-paper] The Gene Ontology Consortium. 2021. + "The Gene Ontology Resource: Enriching a GOld Mine." + *Nucleic Acids Research* 49(D1): D325-D334. + doi:10.1093/nar/gkaa1113 + Available at: `https://pubmed.ncbi.nlm.nih.gov/33290552/ `_ + +.. [#go-2026] The Gene Ontology Consortium. 2026. + "The Gene Ontology Knowledgebase in 2026." + *Nucleic Acids Research* 54(D1): D1779-D1790. + doi:10.1093/nar/gkaf1292 + Available at: `https://academic.oup.com/nar/article/54/D1/D1779/8383826 `_ diff --git a/docs/source/benchmarking/biology_and_life_sciences/lifo.rst b/docs/source/benchmarking/biology_and_life_sciences/lifo.rst index cd2efb66..3a4654c1 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/lifo.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/lifo.rst @@ -23,10 +23,29 @@ Life Ontology (LifO) ======================================================================================================== -The Life Ontology (LifO) is a general-purpose ontology designed to represent the life processes of organisms and their associated entities and relationships. It provides a structured framework for describing common biological features across diverse organisms, including unicellular prokaryotes like E. coli and multicellular organisms such as humans. LifO captures essential biological concepts such as growth, reproduction, metabolism, and adaptation, enabling researchers to model and analyze life processes in a standardized manner. The ontology supports interoperability between biological databases and facilitates the integration of diverse datasets for comparative studies. LifO is particularly useful in systems biology, evolutionary research, and bioinformatics applications, where a unified representation of life processes is essential for data analysis and hypothesis generation. By providing a common vocabulary for life sciences, LifO enhances data sharing, reproducibility, and collaborative research. - -**Example Usage**: -Use LifO to annotate a dataset describing the metabolic pathways of E. coli, linking each pathway to its corresponding life process and associated biological entities, enabling cross-species comparisons and functional analyses. +The Life Ontology (LifO) is a general-purpose ontology designed to +represent the life processes of organisms and their associated entities +and relationships. It provides a structured framework for describing +common biological features across diverse organisms, including +unicellular prokaryotes such as *E. coli* and multicellular organisms +such as humans [#lifo-github]_ [#lifo-bioportal]_. LifO represents life +processes of organisms together with related entities and relations, +providing a common vocabulary for modelling biological phenomena in a +standardized way [#lifo-github]_ [#lifo-bioportal]_. The ontology is +intended as a broad life-science resource that can support interoperable +description of organism-level biological knowledge across different +systems and datasets [#lifo-github]_ [#lifo-bioportal]_. By providing a +shared framework for representing biological processes and related +entities, LifO can support comparative studies, knowledge organization, +and bioinformatics applications that benefit from a common semantic +structure [#lifo-github]_. + +**Example Usage**: Use LifO to annotate a dataset describing organismal +life processes or related biological entities. For example, linking +metabolic or reproductive processes in *E. coli* or human-related +datasets to standardized ontology terms to support consistent +description, comparison, and integration across biological datasets +[#lifo-github]_ [#lifo-bioportal]_. Metrics & Statistics -------------------------- @@ -135,3 +154,13 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#lifo-github] He, Y. 2018. "LifO: An Ontology of the Life of Organism." + GitHub repository. + Available at: `https://github.com/lifeontology/lifo `_ + +.. [#lifo-bioportal] NCBO BioPortal. n.d. "Life Ontology (LIFO)." + Available at: `https://bioportal.bioontology.org/ontologies/LIFO `_ diff --git a/docs/source/benchmarking/biology_and_life_sciences/marinetlo.rst b/docs/source/benchmarking/biology_and_life_sciences/marinetlo.rst index 7248a0c6..cb705af8 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/marinetlo.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/marinetlo.rst @@ -25,7 +25,29 @@ Marine Taxonomy and Life Ontology (MarineTLO) ======================================================================================================== -MarineTLO is a top level ontology, generic enough to provide consistent abstractions or specifications of concepts included in all data models or ontologies of marine data sources and provide the necessary properties to make this distributed knowledge base a coherent source of facts relating observational data with the respective spatiotemporal context and categorical (systematic) domain knowledge. It can be used as the core schema for publishing Linked Data, as well as for setting up integration systems for the marine domain. It can be extended to any level of detail on demand, while preserving monotonicity. For its development and evolution we have adopted an iterative and incremental methodology where a new version is released every two months. For the implementation we use OWL 2, and to evaluate it we use a set of competency queries, formulating the domain requirements provided by the related communities. +MarineTLO is a top-level ontology for the marine domain, designed to +provide consistent abstractions for concepts appearing across marine +data models and ontologies. It provides the properties needed to make a +distributed marine knowledge base a coherent source of facts, relating +observational data to spatiotemporal context and categorical +(systematic) domain knowledge [#marinetlo-site]_ [#marinetlo-paper]_. +It can be used as a core schema for publishing linked data and for +building integration systems for the marine domain [#marinetlo-site]_ +[#marinetlo-paper]_. MarineTLO is generic enough to be extended to +different levels of detail while preserving monotonicity, and it has +been implemented in OWL 2 and evaluated through competency queries that +capture domain requirements provided by related communities +[#marinetlo-site]_ [#marinetlo-doc]_. By providing a shared top-level +semantic framework, MarineTLO supports semantic interoperability and the +integration of heterogeneous marine biodiversity and observation data +across distributed sources [#marinetlo-paper]_ [#marinetlo-site]_. + +**Example Usage**: Use MarineTLO as a core schema to integrate marine +species, observations, habitats, and sampling-event data from multiple +sources, linking each observation to its taxonomic, spatial, and +temporal context to enable semantic querying and interoperable analysis +across marine biodiversity datasets [#marinetlo-paper]_ +[#marinetlo-site]_. Metrics & Statistics -------------------------- @@ -134,3 +156,24 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#marinetlo-site] Institute of Computer Science, FORTH. 2020. + "MarineTLO | A Top Level Ontology for the Marine/Biodiversity Domain." + Available at: `https://projects.ics.forth.gr/isl/MarineTLO/ `_ + +.. [#marinetlo-doc] Tzitzikas, Y., and collaborators. n.d. + "MarineTLO: A Top Level Ontology for the Marine Domain." + Documentation. + Available at: `https://projects.ics.forth.gr/isl/MarineTLO/files/MarineTLO.pdf `_ + +.. [#marinetlo-paper] Tzitzikas, Y., Allocca, C., Bekiari, C., + Marketakis, Y., Fafalios, P., Doerr, M., Minadakis, N., Patkos, T., + and Candela, L. 2016. "Unifying Heterogeneous and Distributed + Information about Marine Species through the Top Level Ontology + MarineTLO." + *Program* 50(1): 16-40. + doi:10.1108/PROG-10-2014-0072 + Available at: `https://www.vliz.be/imisdocs/publications/283055.pdf `_ diff --git a/docs/source/benchmarking/biology_and_life_sciences/mged.rst b/docs/source/benchmarking/biology_and_life_sciences/mged.rst index 3854f28a..aec9885b 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/mged.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/mged.rst @@ -23,10 +23,31 @@ MGED Ontology (MGED) ======================================================================================================== -The MGED Ontology (MGED) is a domain-specific ontology designed to standardize the description of microarray experiments. It provides a structured vocabulary for representing experimental designs, protocols, and data in the context of microarray gene expression studies. The ontology is divided into two components: the MGED Core Ontology, which aligns with the Microarray Gene Expression (MAGE) standard, and the MGED Extended Ontology, which introduces additional classes and associations beyond the MAGE specification. MGED facilitates interoperability between microarray data repositories, enabling researchers to share, compare, and analyze experimental data effectively. By providing a common framework for describing experimental metadata, MGED supports reproducibility, data integration, and meta-analysis in functional genomics research. The ontology is widely used in bioinformatics tools and databases to annotate experimental datasets and ensure compliance with community standards. - -**Example Usage**: -Annotate a microarray experiment with MGED terms to describe the experimental design, sample preparation protocols, and data processing steps, ensuring that the dataset is interoperable with other repositories and tools. +The MGED Ontology (MGED) is a domain-specific ontology developed to +standardize the description of microarray experiments. It provides a +structured vocabulary and semantic framework for representing +experimental designs, protocols, biomaterials, array platforms, and +data-related aspects of microarray gene expression studies +[#mged-paper]_ [#mged-bioportal]_. MGED was developed by the microarray +community to support consistent annotation of experiments and to align +with broader microarray data standards such as MIAME and MAGE +[#mged-paper]_ [#mged-standards]_. The ontology has been described as +including a more stable core aligned with MAGE and an extended part that +adds further terms and associations for richer experimental description +[#mged-fairsharing]_ [#mged-scicrunch]_. MGED facilitates +interoperability between microarray data repositories and tools, +supporting the sharing, comparison, and analysis of experimental data +[#mged-paper]_ [#mged-standards]_. By providing a common framework for +experimental metadata, MGED supports reproducibility, data integration, +and meta-analysis in functional genomics and microarray informatics +[#mged-paper]_ [#mged-standards]_. + +**Example Usage**: Annotate a microarray experiment with MGED terms to +describe the experimental design, sample and biomaterial +characteristics, hybridization and sample-preparation protocols, array +platform, and data-processing steps, so that the dataset can be shared, +interpreted, and compared consistently across repositories and analysis +tools [#mged-paper]_ [#mged-bioportal]_. Metrics & Statistics -------------------------- @@ -135,3 +156,33 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#mged-paper] Whetzel, P. L., Parkinson, H., Causton, H. C., + Fan, L., Fostel, J., Fragoso, G., Game, L., Heiskanen, M., + Morrison, N., Rocca-Serra, P., Sansone, S.-A., and Stoeckert, C. J. Jr. + 2006. "The MGED Ontology: a resource for semantics-based description + of microarray experiments." + *Bioinformatics* 22(7): 866-873. + doi:10.1093/bioinformatics/btl091 + +.. [#mged-standards] Ball, C. A., Brazma, A., Causton, H., + Chervitz, S., Edgar, R., Hingamp, P., Hermjakob, H., Ikeo, K., + Quackenbush, J., Sherlock, G., Spellman, P., Stoekert, C., + Tateno, Y., and Sarkans, U. 2006. "MGED standards: work in progress." + *OMICS* 10(2): 138-144. + Available at: `https://pubmed.ncbi.nlm.nih.gov/16901218/ `_ + +.. [#mged-bioportal] NCBO BioPortal. n.d. + "Microarray and Gene Expression Data Ontology." + Available at: `https://bioportal.bioontology.org/ontologies/MO `_ + +.. [#mged-fairsharing] FAIRsharing. n.d. + "Microarray and Gene Expression Data Ontology." + Available at: `https://fairsharing.org/1193 `_ + +.. [#mged-scicrunch] SciCrunch. n.d. + "MGED Ontology." + Available at: `https://scicrunch.org/resolver/SCR_004484 `_ diff --git a/docs/source/benchmarking/biology_and_life_sciences/mo.rst b/docs/source/benchmarking/biology_and_life_sciences/mo.rst index 693e00b1..873fc148 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/mo.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/mo.rst @@ -23,10 +23,28 @@ Microscopy Ontology (MO) ======================================================================================================== -The Microscopy Ontology (MO) is a domain ontology designed to provide a structured framework for describing microscopy and microanalysis experiments, data, and equipment. MO extends the PMDco ontological framework and enables semantic integration and interoperability of diverse data sources in microscopy research. The ontology covers key concepts such as imaging modalities, sample preparation methods, instrument components, acquisition parameters, and data analysis techniques. MO supports the annotation of experimental workflows, facilitating data sharing, reproducibility, and advanced analysis across various microscopy studies. By providing a standardized vocabulary, MO enables the development of adaptable data applications and cross-experiment comparisons in materials science, biology, and medical research. The ontology is actively maintained and extended to incorporate new microscopy techniques and research requirements. - -**Example Usage**: -Annotate a microscopy dataset with MO terms to specify the imaging modality (e.g., "scanning electron microscopy"), sample preparation method, instrument configuration, and analysis workflow, enabling semantic search and integration with other microscopy data sources. +The Microscopy Ontology (MO) is a domain ontology developed to provide a +structured framework for describing microscopy and microanalysis +experiments, data, and equipment. It extends the PMD Core Ontology +(PMDco) and was developed within the Platform MaterialDigital ecosystem +to support semantic integration and interoperability of microscopy data +[#mo-repo]_ [#mo-paper]_. The ontology covers microscopy-specific +concepts and relationships needed to describe processes, equipment, and +parameters in microscopy and microanalysis workflows [#mo-paper]_ +[#mo-overview]_. MO is intended to improve the semantic representation +of microscopy knowledge and support better query results and logical +linking among related terms and data objects [#mo-paper]_ [#mo-repo]_. +By providing a standardized vocabulary grounded in PMDco, the ontology +supports interoperable data description and integration across +materials-science microscopy datasets and related digital research +infrastructures [#mo-overview]_ [#pmdco-paper]_. + +**Example Usage**: Annotate a microscopy dataset with MO terms to specify +the imaging modality (for example scanning electron microscopy or +transmission electron microscopy), relevant equipment and parameters, +sample-related descriptors, and analysis-related concepts, enabling +semantic search, interoperable data integration, and improved querying +across microscopy data sources [#mo-paper]_ [#mo-repo]_. Metrics & Statistics -------------------------- @@ -135,3 +153,27 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#mo-paper] Bayerlein, B., Schilling, M., Curran, M., and Lau, J. W. + 2024. "Natural Language Processing-Driven Microscopy Ontology + Development." + *Integrating Materials and Manufacturing Innovation*. + doi:10.1007/s40192-024-00378-y + +.. [#mo-repo] materialdigital. n.d. "Microscopy Ontology (MO)." + GitHub repository. + Available at: `https://github.com/materialdigital/microscopy-ontology `_ + +.. [#mo-overview] Bayerlein, B., Schilling, M., Bruns, S., and others. + 2024. "Concepts for a Semantically Accessible Materials Data Space: + Overview over Specific Implementations in Materials Science." + *Advanced Engineering Materials*. + Available at: `https://advanced.onlinelibrary.wiley.com/doi/10.1002/adem.202401092 `_ + +.. [#pmdco-paper] Schilling, M., Bayerlein, B., Birkholz, H., and others. + 2024. "PMD Core Ontology: Achieving Semantic Interoperability in + Materials Science." + *Materials & Design* 237: 112563. diff --git a/docs/source/benchmarking/biology_and_life_sciences/npo.rst b/docs/source/benchmarking/biology_and_life_sciences/npo.rst index 40a52b98..41826a15 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/npo.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/npo.rst @@ -23,10 +23,30 @@ NanoParticle Ontology (NPO) ======================================================================================================== -The NanoParticle Ontology (NPO) is a domain ontology developed within the Basic Formal Ontology (BFO) framework to represent knowledge about the preparation, chemical composition, and characterization of nanomaterials, especially in cancer research and nanomedicine. NPO provides a structured vocabulary for describing nanoparticle types, synthesis methods, physicochemical properties, surface modifications, and biological interactions. The ontology supports semantic annotation of nanomaterial data, enabling interoperability, data integration, and advanced queries across toxicology, biomedical, and materials science databases. NPO is publicly available through BioPortal and is maintained by the National Center for Biomedical Ontology, with ongoing editorial and governance processes for review and growth. By providing a standardized framework, NPO facilitates reproducibility, regulatory compliance, and knowledge sharing in nanotechnology research. The ontology is actively extended to incorporate new nanomaterial types, experimental techniques, and application domains as the field evolves. - -**Example Usage**: -Annotate a nanomedicine study with NPO terms to specify nanoparticle type (e.g., "gold nanoparticle"), synthesis method, surface coating, and biological assay results, enabling cross-study comparison and regulatory reporting. +The NanoParticle Ontology (NPO) is a domain ontology developed within +the Basic Formal Ontology (BFO) framework to represent knowledge about +the preparation, chemical composition, and characterization of +nanomaterials, especially in cancer research and nanomedicine +[#npo-paper]_ [#npo-bioportal]_. NPO provides a structured vocabulary +for describing nanoparticle composition, preparation methods, +physicochemical characteristics, and related entities relevant to +nanotechnology research [#npo-paper]_ [#enanomapper]_. The ontology +supports semantic annotation of nanomaterial data, enabling data +integration, interoperability, and ontology-based querying across +biomedical and nanoinformatics resources [#npo-paper]_ [#enanomapper]_. +NPO is publicly available through NCBO BioPortal and has been used as a +reference ontology in nanomaterial data standardization efforts +[#npo-bioportal]_ [#enanomapper]_. By providing a standardized semantic +framework for nanomaterial representation, NPO supports knowledge +sharing, data reuse, and computational analysis in nanotechnology and +nanomedicine research [#npo-paper]_ [#nanoinformatics]_. + +**Example Usage**: Annotate a nanomedicine study with NPO terms to +specify nanoparticle composition (for example, a gold nanoparticle), +preparation or formulation characteristics, surface functionalization, +and measured physicochemical or biological assay properties, enabling +cross-study comparison, semantic search, and integration across +nanomaterial datasets [#npo-paper]_ [#enanomapper]_. Metrics & Statistics -------------------------- @@ -135,3 +155,30 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#npo-paper] Thomas, D. G., Pappu, R. V., and Baker, N. A. 2011. + "NanoParticle Ontology for Cancer Nanotechnology Research." + *Journal of Biomedical Informatics* 44(1): 59-74. + doi:10.1016/j.jbi.2010.03.001 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC3042056/ `_ + +.. [#npo-bioportal] NCBO BioPortal. n.d. "NanoParticle Ontology (NPO)." + Available at: `https://bioportal.bioontology.org/ontologies/NPO `_ + +.. [#enanomapper] Hastings, J., Jeliazkova, N., Owen, G., Tsiliki, G., + Munteanu, C. R., Steinbeck, C., Willighagen, E., Del Pozo, A., + Džeroski, S., Jeliazkov, V., and others. 2015. + "eNanoMapper: Harnessing Ontologies to Enable Data Integration for + Nanomaterial Risk Assessment." + *Journal of Biomedical Semantics* 6:10. + doi:10.1186/s13326-015-0005-5 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC4374589/ `_ + +.. [#nanoinformatics] Panneerselvam, S., and Choi, S. 2014. + "Nanoinformatics: Emerging Databases and Available Tools." + *International Journal of Molecular Sciences* 15(5): 7158-7182. + doi:10.3390/ijms15057158 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC4057665/ `_ diff --git a/docs/source/benchmarking/biology_and_life_sciences/pato.rst b/docs/source/benchmarking/biology_and_life_sciences/pato.rst index 36d60915..dce64c40 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/pato.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/pato.rst @@ -23,10 +23,26 @@ Phenotype and Trait Ontology (PATO) ======================================================================================================== -The Phenotype and Trait Ontology (PATO) is a structured vocabulary designed to describe phenotypic qualities, attributes, and traits across a wide range of biological organisms. It provides a standardized framework for annotating and analyzing phenotypic data, enabling researchers to compare and integrate data across species and studies. PATO defines qualities such as color, shape, size, and behavior, and links them to specific biological entities and contexts. The ontology is widely used in genetics, developmental biology, and evolutionary studies to describe phenotypic variations and their underlying genetic and environmental factors. By providing a common language for phenotypic traits, PATO facilitates data sharing, integration, and computational analysis in the life sciences. The ontology is actively maintained and updated to incorporate new terms and relationships as research advances. - -**Example Usage**: -Annotate a genetic study with PATO terms to describe phenotypic traits such as "PATO:0000323 (red coloration)" for a flower petal or "PATO:0000383 (increased size)" for a specific organ, enabling cross-study comparisons and meta-analyses. +The Phenotype and Trait Ontology (PATO) is a structured vocabulary for +describing phenotypic qualities, attributes, and traits in a +species-neutral way [#pato-obo]_ [#pato-framework]_. It provides a +standardized framework for annotating and analyzing phenotypic data by +defining qualities such as size, shape, color, morphology, and other +characteristics that can be combined with biological entity ontologies +to describe phenotypes [#pato-framework]_ [#pato-anatomy]_. PATO is +widely used in phenotype annotation and in the logical definition of +phenotype terms across species, supporting data integration and +comparative analysis in genetics, developmental biology, and related +life science domains [#pato-obo]_ [#pato-integration]_. By providing a +common language for phenotypic qualities, PATO facilitates cross-species +interoperability, computational reasoning, and semantic analysis of +phenotype data [#pato-anatomy]_ [#oba-paper]_. + +**Example Usage**: Annotate a genetic or phenotype study with PATO terms +to describe qualities such as red coloration, increased size, abnormal +shape, or altered morphology in association with a specific biological +entity, enabling cross-study comparison, semantic integration, and +computational phenotype analysis [#pato-framework]_ [#pato-integration]_. Metrics & Statistics -------------------------- @@ -135,3 +151,37 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#pato-obo] OBO Foundry. n.d. "Phenotype And Trait Ontology (PATO)." + Available at: `https://obofoundry.org/ontology/pato.html `_ + +.. [#pato-framework] Gkoutos, G. V., Green, E. C. J., Mallon, A.-M., + Hancock, J. M., and Davidson, D. 2005. + "Using Ontologies to Describe Mouse Phenotypes." + *Genome Biology* 6:R8. + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC545487/ `_ + +.. [#pato-integration] Mungall, C. J., Gkoutos, G. V., Smith, C. L., + Haendel, M. A., Lewis, S. E., and Ashburner, M. 2010. + "Integrating Phenotype Ontologies Across Multiple Species." + *Genome Biology* 11:R2. + doi:10.1186/gb-2010-11-1-r2 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC2847714/ `_ + +.. [#pato-anatomy] Gkoutos, G. V., Schofield, P. N., and Hoehndorf, R. + 2018. "The Anatomy of Phenotype Ontologies: Principles, Properties + and Applications." + *Briefings in Bioinformatics* 19(5): 1008-1021. + doi:10.1093/bib/bbx035 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC6169674/ `_ + +.. [#oba-paper] Stefancsik, R., Mungall, C. J., Robinson, P. N., + Smith, C. L., Haendel, M. A., and Gkoutos, G. V. 2023. + "The Ontology of Biological Attributes (OBA)—Computational Traits for + the Life Sciences." + *Database* 2023: baad038. + doi:10.1093/database/baad038 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC9900877/ `_ diff --git a/docs/source/benchmarking/ecology_and_environment/envo.rst b/docs/source/benchmarking/ecology_and_environment/envo.rst index c031c2bb..3a8ba7ab 100644 --- a/docs/source/benchmarking/ecology_and_environment/envo.rst +++ b/docs/source/benchmarking/ecology_and_environment/envo.rst @@ -25,9 +25,31 @@ Environment Ontology (ENVO) ======================================================================================================== -ENVO is a comprehensive, expressive community-driven ontology that enables humans, machines, and semantic web applications to understand environmental entities and concepts across multiple scales, from microscopic organisms to astronomical phenomena. It provides standardized vocabulary for describing environmental features including biomes, ecosystems, habitats, environmental materials (air, water, soil), and environmental conditions (temperature, humidity, pollution levels). ENVO captures relationships between environmental entities and enables precise semantic annotation of environmental datasets, research findings, and monitoring data. As a FAIR-compliant resource, ENVO promotes semantic interoperability in environmental science by providing concise, controlled descriptions of environmental concepts using formal ontological definitions. The ontology supports diverse applications including environmental data management, ecology research, climate science studies, and environmental impact assessment. -**Example Usage**: Annotate an environmental science dataset with ENVO terms such as "tropical rainforest" (biome), "Amazonia" (biogeographic region), and "high soil moisture" (environmental condition) to enable automated discovery of related environmental studies and climate research. +ENVO (Environment Ontology) is a comprehensive, community-driven +ontology for the concise, controlled description of environmental +systems, components, and processes [#envo-obo]_ [#envo-2013]_. It +provides standardized vocabulary for describing environmental features +such as biomes, ecosystems, habitats, environmental materials (for +example air, water, and soil), and environmental conditions [#envo-2016]_ +[#envo-2013]_. ENVO captures semantic relationships between environmental +entities and supports precise annotation of environmental, ecological, +biological, and biomedical datasets [#envo-2013]_ [#envo-2016]_. As an +open, FAIR-enabling ontology resource, ENVO promotes semantic +interoperability by providing formal ontological definitions for +environmental concepts that can be used by humans, machines, and +Semantic Web applications [#envo-obo]_ [#envo-2016]_. The ontology +supports diverse applications including environmental data management, +ecology, biodiversity and microbiome studies, and other research that +requires interoperable environmental descriptions [#envo-2013]_ +[#envo-2016]_. + +**Example Usage**: Annotate an environmental dataset with ENVO terms for +a biome, habitat, environmental material, or environmental condition. For +example, terms describing a tropical rainforest environment, a +biogeographic setting, or elevated soil moisture to enable semantic +search, cross-study integration, and automated discovery of related +environmental and ecological data [#envo-2013]_ [#envo-2016]_. Metrics & Statistics -------------------------- @@ -136,3 +158,24 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#envo-obo] OBO Foundry. n.d. "Environment Ontology (ENVO)." + Available at: `https://obofoundry.org/ontology/envo.html `_ + +.. [#envo-2013] Buttigieg, P. L., Morrison, N., Smith, B., Mungall, C. J., + and Lewis, S. E. 2013. "The Environment Ontology: Contextualising + Biological and Biomedical Entities." + *Journal of Biomedical Semantics* 4:43. + doi:10.1186/2041-1480-4-43 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC3904460/ `_ + +.. [#envo-2016] Buttigieg, P. L., Pafilis, E., Lewis, S. E., + Schildhauer, M. P., Walls, R. L., and Mungall, C. J. 2016. + "The Environment Ontology in 2016: Bridging Domains with Increased + Scope, Semantic Density, and Interoperation." + *Journal of Biomedical Semantics* 7:57. + doi:10.1186/s13326-016-0097-6 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC5035502/ `_ diff --git a/docs/source/benchmarking/ecology_and_environment/oeo.rst b/docs/source/benchmarking/ecology_and_environment/oeo.rst index a305b5bf..f47db892 100644 --- a/docs/source/benchmarking/ecology_and_environment/oeo.rst +++ b/docs/source/benchmarking/ecology_and_environment/oeo.rst @@ -25,10 +25,31 @@ The Open Energy Ontology (OEO) ======================================================================================================== -The Open Energy Ontology (OEO) is a comprehensive domain ontology specifically designed for the energy system analysis context, covering concepts, relationships, and entities relevant to energy research and planning. Developed as part of the Open Energy Platform ecosystem, OEO provides standardized terminology for representing energy systems including generation, transmission, distribution, and consumption across diverse energy sources and technologies. The ontology is expressed in Manchester OWL Syntax to ensure user-friendliness for editing and version control, facilitating collaborative development and maintenance. OEO is actively maintained and continuously extended to incorporate emerging energy concepts, technologies, and regulatory frameworks relevant to energy system analysis and planning. The ontology is governed by a Steering Committee (OEO-SC) that ensures quality, alignment with community needs, and integration with ongoing energy research and policy projects. OEO enables standardized data representation, knowledge integration, and automated reasoning about energy systems for research, policy analysis, and strategic planning. - -**Example Usage**: -Annotate an energy system dataset with OEO terms to describe energy sources (solar, wind, biomass), generation capacity, transmission networks, demand patterns, and storage systems to enable automated analysis of energy system configurations and transition scenarios. +The Open Energy Ontology (OEO) is a domain ontology designed for the +energy system analysis context, covering concepts, relationships, and +entities relevant to energy research and planning [#oeo-paper]_ +[#oeo-site]_. Developed as part of the Open Energy Family and used +within the Open Energy Platform ecosystem, OEO provides standardized +terminology for representing energy systems, including generation, +conversion, transmission, distribution, storage, and consumption +concepts across different technologies and sectors [#oeo-paper]_ +[#oeo-github]_. The ontology is represented in Manchester OWL Syntax, +chosen to support user-friendly editing and version control in +collaborative development workflows [#oeo-paper]_ [#oeo-github]_. OEO is +updated regularly through a release cycle and is continuously extended +to incorporate new concepts relevant to energy system modelling and +analysis [#oeo-site]_ [#oeo-github]_. By providing a shared semantic +framework, OEO supports standardized data annotation, knowledge +integration, semantic search, model interfacing, and automated +reasoning for energy system research and related applications +[#oeo-paper]_ [#oeo-site]_. + +**Example Usage**: Annotate an energy system dataset with OEO terms to +describe energy carriers and sources, generation technologies, +transmission or distribution infrastructure, storage systems, demand +concepts, or scenario-study entities, enabling semantic search, +interoperable data integration, and analysis of energy system +configurations and transition scenarios [#oeo-paper]_ [#oeo-site]_. Metrics & Statistics -------------------------- @@ -137,3 +158,23 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#oeo-paper] Booshehri, M., Emele, L., Flügel, S., Förster, H., + Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., + Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., + Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., + Robinius, M., Sehn, V., and Stappel, M. 2021. + "Introducing the Open Energy Ontology: Enhancing Data Interpretation + and Interfacing in Energy Systems Analysis." + *Energy and AI* 5:100074. + doi:10.1016/j.egyai.2021.100074 + Available at: `https://publications.pik-potsdam.de/pubman/item/item_25641_2/component/file_25642/25641oa.pdf `_ + +.. [#oeo-site] Open Energy Platform. n.d. "OEO Ontology." + Available at: `https://openenergyplatform.org/ontology/ `_ + +.. [#oeo-github] OpenEnergyPlatform. n.d. "Repository for the Open Energy Ontology (OEO)." + Available at: `https://github.com/OpenEnergyPlatform/ontology `_ diff --git a/docs/source/benchmarking/ecology_and_environment/sweet.rst b/docs/source/benchmarking/ecology_and_environment/sweet.rst index f07874af..c07cc814 100644 --- a/docs/source/benchmarking/ecology_and_environment/sweet.rst +++ b/docs/source/benchmarking/ecology_and_environment/sweet.rst @@ -26,8 +26,31 @@ Semantic Web for Earth and Environment Technology Ontology (SWEET) ======================================================================================================== SWEET is a comprehensive collection of interconnected ontologies designed to enhance discovery and utilization of Earth science data through semantic understanding of web resources and Earth system science concepts. It conceptualizes a knowledge space for Earth system science including orthogonal (cross-cutting) concepts such as space, time, Earth realms (atmosphere, hydrosphere, lithosphere), physical quantities, and units, alongside integrative science knowledge concepts such as phenomena, events, and processes. SWEET is represented in OWL (Web Ontology Language) to enable automated reasoning and semantic interoperability in Earth science research. The ontology supports integration of heterogeneous Earth science datasets and models by providing shared semantic definitions across atmospheric science, oceanography, geology, and climate science domains. SWEET facilitates Earth science data discovery and knowledge management by enabling semantic search and automated linking of related datasets and research findings. - -**Example Usage**: Annotate a climate dataset with SWEET terms to describe measured phenomena (e.g., "temperature increase"), spatial context (e.g., "atmosphere" realm, "troposphere" layer), temporal extent (e.g., "1980-2020"), and physical quantities (e.g., "degrees Celsius"). +SWEET (Semantic Web for Earth and Environmental Terminology) is a +comprehensive collection of interconnected ontologies designed to +improve discovery and use of Earth science data through semantic +understanding of web resources and Earth system science concepts +[#sweet-paper]_ [#sweet-repo]_. It conceptualizes a knowledge space for +Earth system science that includes cross-cutting concepts such as +space, time, Earth realms, phenomena, physical quantities, and units, +alongside more domain-specific scientific concepts [#sweet-paper]_ +[#sweet-repo]_. SWEET is represented in OWL and organized as a highly +modular ontology suite, enabling semantic interoperability and automated +reasoning in Earth and environmental science applications +[#sweet-paper]_ [#sweet-repo]_. The ontology supports integration of +heterogeneous Earth science datasets and models by providing shared +semantic definitions across domains such as atmospheric science, +oceanography, geology, and climate science [#sweet-paper]_ +[#sweet-search]_. By providing a shared semantic framework, SWEET +supports Earth science data discovery, semantic search, and knowledge +management across distributed datasets and services [#sweet-paper]_ +[#sweet-search]_. + +**Example Usage**: Annotate a climate or Earth observation dataset with +SWEET terms to describe observed phenomena, Earth realm or layer, +spatial and temporal context, and relevant physical quantities and +units, enabling semantic search, automated linking of related datasets, +and interoperable Earth science analysis [#sweet-paper]_ [#sweet-repo]_. Metrics & Statistics -------------------------- @@ -136,3 +159,22 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#sweet-paper] Raskin, R. G., and Pan, M. J. 2005. + "Knowledge Representation in the Semantic Web for Earth and + Environmental Terminology (SWEET)." + *Computers & Geosciences* 31(9): 1119-1125. + Available at: `https://www.sciencedirect.com/science/article/pii/S0098300405001020 `_ + +.. [#sweet-repo] ESIP Federation. n.d. + "SWEET: Official repository for Semantic Web for Earth and + Environmental Terminology Ontologies." + Available at: `https://github.com/ESIPFed/sweet `_ + +.. [#sweet-search] Pouchard, L. C., Huhns, M. N., and McGuinness, D. L. + 2013. "Linking Earth and Climate Science to Support Semantic Search." + *Semantic Web*. + Available at: `https://semantic-web-journal.net/content/linking-earth-and-climate-science-semantic-search-supporting-investigation-climate-change `_ diff --git a/docs/source/benchmarking/finance/goodrelations.rst b/docs/source/benchmarking/finance/goodrelations.rst index 699ce0c3..396a6e41 100644 --- a/docs/source/benchmarking/finance/goodrelations.rst +++ b/docs/source/benchmarking/finance/goodrelations.rst @@ -25,9 +25,32 @@ Good Relations Language Reference (GoodRelations) ======================================================================================================== -GoodRelations is a widely-used ontology for describing products, services, offers, and commercial entities on the Web. It provides a rich vocabulary for modeling commercial information such as Products, Offers, Stores, Sellers, Payment and Delivery options, Price specifications, and Availability. GoodRelations emphasizes machine-actionable, fine-grained descriptions that support e-commerce discovery, comparison shopping, and automated processing by search engines, marketplaces, and recommendation systems. Key characteristics include a modular class structure (distinguishing Products/Services from Offers and Sellers), detailed modeling of price specifications (including currency, unit price, and price components), temporal validity of offers, and explicit representation of delivery and payment methods. The ontology is designed for interoperability: it can be embedded in HTML pages (microdata/RDFa/JSON-LD), linked with vocabularies like schema.org and FOAF, and exported in RDF/OWL for semantic-web use. GoodRelations supports provenance and business metadata, enabling trust and auditing use cases in marketplaces. Typical applications include SEO and product rich snippets, integration of catalog data across vendors, automated price aggregation, and semantic matching in recommender systems. - -**Example usage**: describe a product offering as an gr:Offering that links to a gr:Product (with identifiers and brand), includes a gr:UnitPriceSpecification (with priceCurrency, price, and validFrom/validThrough), and connects to a gr:BusinessEntity representing the seller with contact details and opening hours. +GoodRelations is a widely used ontology for describing products, +services, offers, and commercial entities on the Web [#gr-paper]_ +[#gr-wiki]_. It provides a rich vocabulary for modeling commercial +information such as offers, business entities, price specifications, +availability, payment options, and delivery methods [#gr-paper]_ +[#gr-ref]_. GoodRelations emphasizes machine-processable, fine-grained +descriptions of e-commerce information that support product discovery, +comparison, and automated processing on the Web [#gr-paper]_ [#gr-wiki]_. +A key design principle is the distinction between products or services, +the offers made for them, and the legal entities that provide them, +together with detailed modeling of prices and commercial conditions +[#gr-paper]_ [#gr-ref]_. The ontology is designed for interoperability +and can be used in RDF/OWL as well as embedded in Web pages; it also +influenced and was integrated into the schema.org e-commerce model +[#gr-wiki]_ [#schema-releases]_. By providing a shared semantic +framework for commercial data, GoodRelations supports e-commerce SEO, +catalog integration, offer aggregation, and other Semantic Web and Web +data applications [#gr-paper]_ [#gr-wiki]_. + +**Example Usage**: Describe a product offering as a +``gr:Offering`` that links to a product or service, includes a +``gr:UnitPriceSpecification`` with currency and price information, and +connects to a ``gr:BusinessEntity`` representing the seller together +with relevant payment, delivery, and offer-validity information, so +that the offering can be processed consistently by search engines, +marketplaces, and other web applications [#gr-ref]_ [#gr-paper]_. Metrics & Statistics -------------------------- @@ -136,3 +159,25 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#gr-paper] Hepp, M. 2008. + "GoodRelations: An Ontology for Describing Products and Services + Offers on the Web." + In *Knowledge Engineering: Practice and Patterns*, Lecture Notes in + Computer Science 5268, pp. 329-346. + Available at: `https://www.heppnetz.de/files/GoodRelationsEKAW2008-crc-final.pdf `_ + +.. [#gr-ref] Hepp, M. 2010. + "GoodRelations Language Reference." + Available at: `https://www.heppnetz.de/ontologies/goodrelations/20100412/v1.html `_ + +.. [#gr-wiki] GoodRelations Wiki. n.d. + "Documentation/Intro." + Available at: `https://wiki.goodrelations-vocabulary.org/Documentation/Intro `_ + +.. [#schema-releases] Schema.org. 2026. + "Schema.org Releases." + Available at: `https://schema.org/docs/releases.html `_ diff --git a/metadata/ontolearner-metadata.rdf b/metadata/ontolearner-metadata.rdf index 39f2033e..32c83dac 100644 --- a/metadata/ontolearner-metadata.rdf +++ b/metadata/ontolearner-metadata.rdf @@ -5,32 +5,104 @@ This Dublin Core metadata collection describes ontologies benchmarked in OntoLearner. It includes information such as title, creator, format, license, and version. OntoLearner Team MIT License - 1.5.0 + 1.5.1 - - MusicOntology - Music Ontology (MusicOntology) - The Music Ontology Specification provides main concepts and properties fo describing music (i.e. artists, albums and tracks) on the Semantic Web. - Knowledge Media Institute, Open University + + UMBEL + Upper Mapping and Binding Exchange Layer Vocabulary (UMBEL) + UMBEL is the Upper Mapping and Binding Exchange Layer, designed to help content interoperate on the Web. UMBEL provides two valuable functions: First, it is a broad, general reference structure of 34,000 concepts, which provides a scaffolding to link and interoperate other datasets and domain vocabularies. Second, it is a base vocabulary for the construction of other concept-based domain ontologies, also designed for interoperation. + n3 + May 10, 2016 + https://github.com/structureddynamics/UMBEL/tree/master/Ontology + General Knowledge + Web Development + 1.50 + + + AIISO + Academic Institution Internal Structure Ontology (AIISO) + The Academic Institution Internal Structure Ontology (AIISO) provides classes and properties to describe the internal organizational structure of an academic institution. AIISO is designed to work in partnership with Participation (http://purl.org/vocab/participation/schema), FOAF (http://xmlns.com/foaf/0.1/) and aiiso-roles (http://purl.org/vocab/aiiso-roles/schema) to describe the roles that people play within an institution. + Open University RDF - 2013/07/22 + 2008-05-14 Creative Commons 4.0 - https://github.com/motools/musicontology - Arts and Humanities - Music Theory - 2.1.5 + https://vocab.org/aiiso/ + Scholarly Knowledge + Academic Institution + 1.0 - - PODO - Point Defects Ontology (PODO) - PODO focuses on the description of point defects in crystalline materials. - https://orcid.org/0000-0001-7564-7990 + + PRotein + Protein Ontology (PRO) + The PRotein Ontology (PRO) formally defines taxon-specific and taxon-neutral protein-related entities in three major areas: proteins related by evolution; proteins produced from a given gene; and protein-containing complexes. + RDF + 08:08:2024 + Creative Commons 4.0 + http://purl.obolibrary.org/obo/pr.owl + Medicine + Protein + 1.2 + + + MAMBO + Molecules And Materials Basic Ontology (MAMBO) + MAMBO (Molecules And Materials Basic Ontology) is a domain ontology for molecular materials. Its main targets are: Allowing the retrieval of structured information regarding molecular materials and related applications (i.e. devices based on molecular materials) Supporting the development of new, complex workflows for modelling systems based on molecular materials (computational modelling and data-driven techniques) Integrating data generated via computational simulations and empirical experiments. OWL - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/OCDO/podo + General Public License v3.0 (GPL-3.0) + https://github.com/daimoners/MAMBO Materials Science and Engineering Materials Science - 1.0.0 + + + AUTO + Automotive Ontology (AUTO) + The AUTOMOTIVE ONTOLOGY (AUTO) defines the shared conceptual structures in the automotive industry. It is an OWL ontology. It is built upon the auto schema.org extension created by the W3C Automotive Ontology Community Group. AUTO's development process follows the best practices established by the EDMC FIBO Community. + EDM Council + RDF + 2021-03-01 + MIT + https://github.com/edmcouncil/auto/tree/master + Industry + Automotive + + + SWO + Software Ontology (SWO) + The Software Ontology (SWO) is a resource for describing software tools, their types, tasks, versions, provenance and associated data. It contains detailed information on licensing and formats as well as software applications themselves, mainly (but not limited) to the bioinformatics community. + Allyson Lister, Andy Brown, Duncan Hull, Helen Parkinson, James Malone, Jon Ison, Nandini Badarinarayan, Robert Stevens + OWL + 2013-07-01 + Creative Commons 4.0 + https://terminology.tib.eu/ts/ontologies/SWO + Scholarly Knowledge + Software + 1.0 + + + BBCWildlife + BBC Wildlife Ontology (BBCWildlife) + A simple vocabulary for describing biological species and related taxa. The vocabulary defines terms for describing the names and ranking of taxa, as well as providing support for describing their habitats, conservation status, and behavioural characteristics, etc. + https://www.ldodds.com#me, http://tomscott.name/ + TTL + 2013/12/18 + Creative Commons 4.0 + https://www.bbc.co.uk/ontologies/wildlife-ontology + News and Media + Wildlife + 1.1 + + + ENVO + Environment Ontology (ENVO) + ENVO is an expressive, community ontology which helps humans, machines, and semantic web applications understand environmental entities of all kinds, from microscopic to intergalactic scales. As a FAIR-compliant resource, it promotes interoperability through the concise, controlled description of all things environmental. + Pier Luigi Buttigieg (https://orcid.org/0000-0002-4366-3088) + OWL + 2024-07-01 + Creative Commons 1.0 + https://obofoundry.org/ontology/envo.html + Ecology and Environment + Environment, Ecosystems, Habitats + 2024-07-01 DOID @@ -44,56 +116,6 @@ Medicine Human Diseases - - OntoCAPE - Ontology of Computer-Aided Process Engineering (OntoCAPE) - OntoCAPE is a large-scale ontology for the domain of Computer Aided Process Engineering (CAPE). Represented in a formal, machine-interpretable ontology language, OntoCAPE captures consensual knowledge of the process engineering domain in a generic way such that it can be reused and shared by groups of people and across software systems. On the basis of OntoCAPE, novel software support for various engineering activities can be developed; possible applications include the systematic management and retrieval of simulation models and design documents, electronic procurement of plant equipment, mathematical modeling, as well as the integration of design data from distributed sources. - RWTH Aachen University - OWL - GNU General Public License. - https://www.avt.rwth-aachen.de/cms/avt/forschung/sonstiges/software/~ipts/ontocape/?lidx=1 - Materials Science and Engineering - Manufacturing - 2.0 - - - GTS - Geologic Timescale model (GTS) - This is an RDF/OWL representation of the GeoSciML Geologic Timescale model, which has been adapted from the model described in Cox, S.J.D, & Richard, S.M. (2005) A formal model for the geologic timescale and GSSP, compatible with geospatial information transfer standards, Geosphere, Geological Society of America. - Simon J D Cox (simon.cox@csiro.au) of CSIRO - TTL - 2020-05-31 - Creative Commons 1.0 - https://raw.githack.com/CGI-IUGS/timescale-ont/master/html/gts.html - Geography - geospatial Information, Geology - 1.0 - - - GIST - GIST Upper Ontology (GIST) - Gist is Semantic Arts' minimalist upper ontology for the enterprise. It is designed to have the maximum coverage of typical business ontology concepts with the fewest number of primitives and the least amount of ambiguity. - Semantic Arts - RDF - 2024-Feb-27 - Creative Commons 4.0 - https://semanticarts.com/gist - General Knowledge - Upper Ontology - 12.1.0 - - - PLDO - Planar Defects Ontology (PLDO) - PLDO is an ontology designed to describe planar defects in crystalline materials, such as grain boundaries and stacking faults, with a focus on their atomic-scale structure and properties. - https://orcid.org/0000-0001-7564-7990 - OWL - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/OCDO/pldo - Materials Science and Engineering - Materials Defects - 1.0.0 - PRIMA PRovenance Information in MAterials science (PRIMA) @@ -107,190 +129,127 @@ Materials Science 2.0 - - RO - Relation Ontology (RO) - The Relations Ontology (RO) is a collection of OWL relations (ObjectProperties) intended for use across a wide variety of biological ontologies. - OWL - 2024-04-24 - CC0 - http://purl.obolibrary.org/obo/ro.owl - General Knowledge - Relations - 2024-04-24 - - - ICON - Icon Ontology (ICON) - The ICON ontology deals with high granularity art interpretation. It was developed by conceptualizing Panofsky's theory of levels of interpretation, therefore artworks can be described according to Pre-iconographical, Iconographical and Iconological information. - Knowledge Media Institute - OWL - April 26th, 2024 + + QUDT + Quantities, Units, Dimensions and Data Types (QUDT) + QUDT is an advocate for the development and implementation of standards to quantify data expressed in RDF and JSON. + NASA Ames Research Center + TTL + March 1, 2022 Creative Commons 4.0 - https://w3id.org/icon/ontology/ - Arts and Humanities - Art History, Cultural Heritage - 2.1.0 + https://qudt.org/ + Units and Measurements + Physics + 2.1 - - CiTO - Citation Typing Ontology (CiTO) - The Citation Typing Ontology (CiTO) is an ontology that enables characterization of the nature or type of citations, both factually and rhetorically. - Silvio Peroni, David Shotton + + MaterialInformation + Material Information Ontology (MaterialInformation) + The Material Information ontology is divided into smaller ontologies (partitions). The partitions are Environment, Geometry, Material Information, Manufacturing Process, Property, Substance, Unit Dimension, Structure, Equation and Physical Constant. + Toshihiro Ashino OWL - 2018-02-16 - Creative Commons 4.0 - https://github.com/SPAROntologies/cito/tree/master/docs/current - Scholarly Knowledge - Scholarly Communication - 2.8.1 - - - MO - Microscopy Ontology (MO) - The Microscopy Ontology (MO) extends the ontological framework of the PMDco. The MO facilitates semantic integration and the interoperable connection of diverse data sources from the fields of microscopy and microanalysis. Consequently, the MO paves the way for new, adaptable data applications and analyses across various experiments and studies - https://orcid.org/0000-0002-3717-7104,https://orcid.org/0000-0002-7094-5371 - TTL - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/materialdigital/microscopy-ontology?tab=readme-ov-file - Biology and Life Sciences - Microscopy - 2.0 - - - GPO - General Process Ontology (GPO) - Basically, this ontology aims to model processes. Processes are holistic perspective elements that transform inputs/educts (matter, energy, information) into output/products (matter, energy, information) with the help of tools (devices, algorithms). They can be decomposed into sub-processes and have predecessor and successor processes. - Simon Stier - TTL - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/General-Process-Ontology/ontology + https://github.com/EngyNasr/MSE-Benchmark/blob/main/testCases/secondTestCase/MaterialInformation.owl Materials Science and Engineering Materials Science - - MDO - Materials Design Ontology (MDO) - MDO is an ontology for materials design field, representing the domain knowledge specifically related to solid-state physics and computational materials science. - Materials Design Division, National Institute for Materials Science (NIMS) + + SIOC + Semantically-Interlinked Online Communities (SIOC) + The SIOC (Semantically-Interlinked Online Communities) Ontology is an ontology for describing the information in online communities. This includes sites that support online discussions, blogging, file sharing, photo sharing, social networking, etc. + Data Science Institute, NUI Galway + RDF + 2018/02/28 + Creative Commons 3.0 + http://rdfs.org/sioc/spec/ + Social Sciences + Social Networks + 1.36 + + + MicroStructures + EMMO-based ontology for microstructures (MicroStructures) + This is intended to be a domain ontology for metallic microstructures, covering aspects like: composition, particles, both stable (primary) and metastable (precipitates), grains, subgrains, grain boundaries & particle free zones (PFZs), texture, dislocations. The aim is to support both microstructure modelling as well as characterisation. OWL - 2022-08-02 - Creative Commons 4.0 - https://github.com/LiUSemWeb/Materials-Design-Ontology/tree/master/ + https://github.com/jesper-friis/emmo-microstructure Materials Science and Engineering - Materials Design - 1.1 + Microstructure - - MatWerk - NFDI MatWerk Ontology (MatWerk) - NFDI-MatWerk aims to establish a digital infrastructure for Materials Science and Engineering (MSE), fostering improved data sharing and collaboration. This repository provides comprehensive documentation for NFDI MatWerk Ontology (MWO) v3.0, a foundational framework designed to structure research data and enhance interoperability within the MSE community. To ensure compliance with top-level ontology standards, MWO v3.0 is aligned with the Basic Formal Ontology (BFO) and incorporates the modular approach of the NFDIcore mid-level ontology, enriching metadata through standardized classes and properties. The MWO addresses key aspects of MSE research data, including the NFDI-MatWerk community structure, covering task areas, infrastructure use cases, projects, researchers, and organizations. It also describes essential NFDI resources, such as software, workflows, ontologies, publications, datasets, metadata schemas, instruments, facilities, and educational materials. Additionally, MWO represents NFDI-MatWerk services, academic events, courses, and international collaborations. As the foundation for the MSE Knowledge Graph, MWO facilitates efficient data integration and retrieval, promoting collaboration and knowledge representation across MSE domains. This digital transformation enhances data discoverability, reusability, and accelerates scientific exchange, innovation, and discoveries by optimizing research data management and accessibility. - Hossein Beygi Nasrabadi, Jörg Waitelonis, Ebrahim Norouzi, Kostiantyn Hubaiev, Harald Sack + + BBCCoreConcepts + BBC Core Concepts Ontology (BBCCoreConcepts) + The generic BBC ontology for people, places, events, organisations, themes which represent things that make sense across the BBC. This model is meant to be generic enough, and allow clients (domain experts) link their own concepts e.g., athletes or politicians using rdfs:sublClassOf the particular concept. + jeremy.tarling@bbc.co.uk, tom.hodgkinson@bbc.co.uk TTL - 2025-03-01 - Creative Commons 1.0 - https://github.com/ISE-FIZKarlsruhe/mwo?tab=readme-ov-file - Materials Science and Engineering - Research Data, Interoperability - 3.0.0 - - - BioPAX - Biological Pathways Exchange (BioPAX) - BioPAX is a standard language that aims to enable integration, exchange, visualization and analysis of biological pathway data. Specifically, BioPAX supports data exchange between pathway data groups and thus reduces the complexity of interchange between data formats by providing an accepted standard format for pathway data. - OWL - 16 April 2015 - http://www.biopax.org/ - Biology and Life Sciences - Bioinformatics - 1.0 - - - VIMMP - Virtual Materials Marketplace Ontologies (VIMMP) - The Virtual Materials Marketplace (VIMMP) project is developing an open platform for providing and accessing services related to materials modelling. Within VIMMP, a system of marketplace-level ontologies is developed to characterize services, models, and interactions between users; the European Materials and Modelling Ontology (EMMO, recently renamed while keeping the original acronym) is employed as a top-level ontology. The ontologies are used to annotate data that are stored in the ZONTAL Space component of VIMMP and to support the ingest and retrieval of data and metadata at the VIMMP marketplace front-end. - Ilian T. Todorov, Martin Thomas Horsch, Michael A. Seaton, Silvia Chiacchiera - OWL - 2021-01-02 - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://matportal.org/ontologies/VIMMP_ONTOLOGIES - Materials Science and Engineering - Materials Modeling - - - AGROVOC - AGROVOC Multilingual Thesaurus (AGROVOC) - AGROVOC is a relevant Linked Open Data set about agriculture available for public use and facilitates access and visibility of data across domains and languages. It offers a structured collection of agricultural concepts, terms, definitions and relationships which are used to unambiguously identify resources, allowing standardized indexing processes and making searches more efficient. - Food and Agriculture Organization of the United Nations - RDF - August 12, 2024 + 2019-11-21 Creative Commons 4.0 - https://agroportal.lirmm.fr/ontologies/AGROVOC - Agriculture - Agricultural Knowledge - 2024-04 + https://www.bbc.co.uk/ontologies/core-concepts-ontology + News and Media + Core Concepts + 1.30 - - CopyrightOnto - Copyright Ontology (CopyrightOnto) - The Copyright Ontology tries to formalise the copyright domain as a way to facilitate automated (or computer-supported) copyright management through the whole content value chain, as it is shaped by copyright law. Therefore, it does not focus just on the last step, end-users permissions to consume content, like many rights languages and ontologies do. - Rhizomik - TTL - 2019-09 - Creative Commons 4.0 - https://rhizomik.net/ontologies/copyrightonto/ - Law - Legal Knowledge + + GeoNames + GeoNames Ontology (GeoNames) + The Geonames ontologies provides elements of description for geographical features, in particular those defined in the geonames.org database. + Bernard Vatant + RDF + 2022-01-30 + Creative Commons 3.0 + https://www.geonames.org/ontology + Geography + Geographic Knowledge + 3.3 - - FRBRoo - Functional Requirements for Bibliographic Records - object-oriented (FRBRoo) - The FRBRoo (Functional Requirements for Bibliographic Records - object-oriented) initiative is a joint effort of the CIDOC Conceptual Reference Model and Functional Requirements for Bibliographic Records international working groups to establish a formal ontology intended to capture and represent the underlying semantics of bibliographic information and to facilitate the integration, mediation, and interchange of bibliographic and museum information. + + SAREF + Smart Applications REFerence ontology (SAREF) + The Smart Applications REFerence (SAREF) suite of ontologies forms a shared model of consensus intended to enable semantic interoperability between solutions from different providers and among various activity sectors in the Internet of Things (IoT), thus contributing to the development of data spaces. SAREF is published as a set of open standards produced by ETSI Technical Committee Smart Machine-to-Machine communications (SmartM2M). + ETSI Technical Committee Smart Machine-to-Machine communications (SmartM2M) RDF - November 2015 - Creative Commons 4.0 - https://ontome.net/namespace/6#summary - Scholarly Knowledge - Bibliographic Records - 2.4 + 2020-12-31 + https://saref.etsi.org/core/v3.2.1/ + Web and Internet + interoperability + 3.2.1 - - CSO - Computer Science Ontology (CSO) - The Computer Science Ontology (CSO) is a large-scale ontology of research areas in computer science. It provides a comprehensive vocabulary of research topics in computing, organized in a hierarchical structure. This class processes the Computer Science Ontology (CSO) with custom hooks for: - Topic-based class detection - superTopicOf relationships - contributesTo relationships - Knowledge Media Institute, Open University - OWL - Creative Commons 4.0 - https://cso.kmi.open.ac.uk/home - Scholarly Knowledge - Computer Science - 3.4 + + MDS + Materials Data Science Ontology (MDS) + Materials Data Science (MDS) is an ontology encompassing multiple domains relevant to materials science, chemical synthesis and characterizations, photovoltaics and geospatial datasets. The terms used for classes, subclasses and instances are mapped to PMDCo and BFO Ontologies. + SDLE Research Center + TTL + 03/24/2024 + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://matportal.org/ontologies/MDS + Materials Science and Engineering + Materials Science + 0.3.0.0 - - DataCite - DataCite Ontology (DataCite) - The DataCite Ontology (DataCite) is an ontology that enables the metadata properties of the DataCite Metadata Schema Specification (i.e., a list of metadata properties for the accurate and consistent identification of a resource for citation and retrieval purposes) to be described in RDF. - David Shotton, Silvio Peroni - RDF - 15/09/2022 - Creative Commons 4.0 - https://schema.datacite.org/ - Scholarly Knowledge - Metadata - 3.1 + + AFO + Allotrope Foundation Ontology (AFO) + The AFO is an ontology suite that provides a standard vocabulary and semantic model for the representation of laboratory analytical processes. The AFO suite is aligned at the upper layer to the Basic Formal Ontology (BFO). The core domains modeled include, Equipment, Material, Process, and Results. This artifact contains all triples of Allotrope Foundation Merged Without QUDT Ontology Suite (REC/2023/12) together with triples inferred with HermiT. + Allotrope Foundation + TTL + 2024-06-28 + CC BY 4.0 + https://terminology.tib.eu/ts/ontologies/AFO + Chemistry + Laboratory Analytical Processes + 2024-06 - - BBCCMS - BBC CMS Ontology (BBCCMS) - The Content Management Systems ontology defines the terms that LDP needs to interact with systems that produce content. The linked data platform contain semantic metadata for the creative content and also the things the BBC produces content about. The CMS ontology defines how these things and content are associated with other BBC instances of the same thing. + + BBCCreativeWork + BBC Creative Work Ontology (BBCCreativeWork) + This ontology defines the terms required to describe the creative works produced by the BBC and their associated metadata. This ontology powers reading and writing creative works in the triplestore using tags associated with them (about) their more specific types (BlogPost, NewsItem, Programme) and audiences (audience). LinkedData@bbc.co.uk TTL 2012-12-01 Creative Commons 4.0 - https://www.bbc.co.uk/ontologies/cms-ontology + https://www.bbc.co.uk/ontologies/creative-work-ontology News and Media - Content Management Systems - 3.7 + Creative Work + 1.19 FSO @@ -305,154 +264,179 @@ Materials Science 0.1.0 - - LIFO - Life Ontology (LifO) - The Life Ontology (LifO) is an ontology of the life of organism. LifO represents the life processes of organisms and related entities and relations. LifO is a general purpose ontology that covers the common features associated with different organisms such as unicellular prokaryotes (e.g., E. coli) and multicellular organisms (e.g., human). - Yongqun "Oliver" He (YH) + + Contact + Contact Ontology (Contact) + Ontology to capture concepts related to contact information (addresses, phone numbers). Reuses the iContact Ontology developed by the Enterprise Integration Lab in Toronto. The iContact ontology is extended to introduce a specialized definition of Hours of Operation, defined as a subclass of both the iContact definition of hours of operation, and a subclass of the Recurring Event class defined in the iCity Recurring Event ontology. The Contact ontology also extends the definition of address to include an associated location. + Mark Fox, Megan Katsumi + RDF + 2018-07-06 + https://enterpriseintegrationlab.github.io/icity/Contact/Contact_1.0/doc/index-en.html + Social Sciences + Social + 1.0 + + + GO + Gene Ontology (GO) + The Gene Ontology (GO) Provides structured controlled vocabularies for the annotation of gene products with respect to their molecular function, cellular component, and biological role. OWL - March 11, 2018 + 2024-11-03 Creative Commons 4.0 - https://bioportal.bioontology.org/ontologies/LIFO + https://geneontology.org/docs/download-ontology/ Biology and Life Sciences - General Purpose - 1.0.17 + Molecular Biology, Genetics - - MDS - Materials Data Science Ontology (MDS) - Materials Data Science (MDS) is an ontology encompassing multiple domains relevant to materials science, chemical synthesis and characterizations, photovoltaics and geospatial datasets. The terms used for classes, subclasses and instances are mapped to PMDCo and BFO Ontologies. - SDLE Research Center - TTL - 03/24/2024 - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://matportal.org/ontologies/MDS + + FIX + FIX Ontology (FIX) + An ontology of physico-chemical methods and properties. + OWL + 2020-04-13 + https://terminology.tib.eu/ts/ontologies/FIX + Chemistry + Chemicals, Properties + 2020-04-13 + + + MatOnto + Material Ontology (MatOnto) + The Material Ontology (MatOnto) is based on the upper level ontology, the BFO. + OWL + https://github.com/EngyNasr/MSE-Benchmark/blob/main/testCases/secondTestCase/MatOnto.owl Materials Science and Engineering - Materials Science - 0.3.0.0 + Scholarly Knowledge - - BBCWildlife - BBC Wildlife Ontology (BBCWildlife) - A simple vocabulary for describing biological species and related taxa. The vocabulary defines terms for describing the names and ranking of taxa, as well as providing support for describing their habitats, conservation status, and behavioural characteristics, etc. - https://www.ldodds.com#me, http://tomscott.name/ + + TribAIn + Tribology and Artificial Intelligence Ontology (TribAIn) + TribAIn is an ontology for the description of tribological experiments and their results. It is designed to be used in the context of the TribAIn project, which aims to develop a knowledge-based system for the design of tribological systems. + Patricia Kügler TTL - 2013/12/18 Creative Commons 4.0 - https://www.bbc.co.uk/ontologies/wildlife-ontology - News and Media - Wildlife - 1.1 - - - WiLD - Workflows in Linked Data (WiLD) - Ontology to describe Workflows in Linked Data. - Tobias Käfer - TTL - 2020-06-10 - DBpedia License - https://databus.dbpedia.org/ontologies/purl.org/wild--vocab/2020.06.10-210552 + https://github.com/snow0815/tribAIn Scholarly Knowledge - Materials Science - - CDCO - Crystallographic Defect Core Ontology (CDCO) - CDCO defines the common terminology shared across all types of crystallographic defects, providing a unified framework for data integration in materials science. - https://orcid.org/0000-0001-7564-7990 - OWL - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/OCDO/cdco - Materials Science and Engineering - Materials Science - 1.0.0 + + DataCite + DataCite Ontology (DataCite) + The DataCite Ontology (DataCite) is an ontology that enables the metadata properties of the DataCite Metadata Schema Specification (i.e., a list of metadata properties for the accurate and consistent identification of a resource for citation and retrieval purposes) to be described in RDF. + David Shotton, Silvio Peroni + RDF + 15/09/2022 + Creative Commons 4.0 + https://schema.datacite.org/ + Scholarly Knowledge + Metadata + 3.1 - - Photovoltaics - EMMO Domain Ontology for Photovoltaics (Photovoltaics) - This ontology is describing Perovskite solar cells. - Casper Welzel Andersen, Simon Clark - TTL - Creative Commons license Attribution 4.0 International (CC BY 4.0) - https://github.com/emmo-repo/domain-photovoltaics - Materials Science and Engineering - Materials Science - 0.0.1 + + DOLCE + Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) + The Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) is a foundational ontology that provides a conceptual framework for the formalization of domain ontologies. + Laboratory for Applied Ontology, ISTC-CNR + OWL + Creative Commons 4.0 + https://www.loa.istc.cnr.it/index.php/dolce/ + Upper Ontology + Linguistics, Cognitive Science - - TUBES - TUBES System Ontology (TUBES) - The scope of the TUBES System Ontology is to explicitly define interconnected building service system in the AECO industry, their hierarchical subdivisions, structural and functional aspects, and links to spatial entities. As such, TSO supports the effort to represent linkable information in a future semantic web of building data. It has a strong alignment to other ontologies within the W3C community. - Nicolas Pauen + + FOAF + Friend of a Friend (FOAF) + FOAF is a project devoted to linking people and information using the Web. Regardless of whether information is in people's heads, in physical or digital documents, or in the form of factual data, it can be linked. + Dan Brickley, Libby Miller RDF - 2022-02-01 + 14 January 2014 + Creative Commons + http://xmlns.com/foaf/0.1/ + Social Sciences + Social + 0.1 + + + MarineTLO + Marine Taxonomy and Life Ontology (MarineTLO) + MarineTLO is a top level ontology, generic enough to provide consistent abstractions or specifications of concepts included in all data models or ontologies of marine data sources and provide the necessary properties to make this distributed knowledge base a coherent source of facts relating observational data with the respective spatiotemporal context and categorical (systematic) domain knowledge. It can be used as the core schema for publishing Linked Data, as well as for setting up integration systems for the marine domain. It can be extended to any level of detail on demand, while preserving monotonicity. For its development and evolution we have adopted an iterative and incremental methodology where a new version is released every two months. For the implementation we use OWL 2, and to evaluate it we use a set of competency queries, formulating the domain requirements provided by the related communities. + Information System Laboratory (ISL), Institute of Computer Science (ICS), Foundation for Research and Technology - Hellas (FORTH) + OWL + 2017-01-05 Creative Commons 4.0 - https://rwth-e3d.github.io/tso/ - Industry - Building Services - 0.3.0 + https://projects.ics.forth.gr/isl/MarineTLO/ + Biology and Life Sciences + Marine Science, Oceanography + 1.0 - - IAO - Information Artifact Ontology (IAO) - The Information Artifact Ontology (IAO) is an ontology of information entities, originally driven by work by the OBI digital entity and realizable information entity branch. + + PPlan + Ontology for Provenance and Plans (P-Plan) + The Ontology for Provenance and Plans (P-Plan) is an extension of the PROV-O ontology [PROV-O] created to represent the plans that guided the execution of scientific processes. P-Plan describes how the plans are composed and their correspondence to provenance records that describe the execution itself. + http://www.isi.edu/~gil/ OWL - 2022-11-07 + 2014-03-12 Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/IAO - General Knowledge - Information, Data, Knowledge + https://vocab.linkeddata.es/p-plan/index.html + Scholarly Knowledge + 1.3 - - MDSOnto - The Modular Ontology for Materials and Data Science (MDS-Onto) - MDS-Onto is a domain (low) level ontology that describes terms in Materials Data Science. It is divided into six large modules: BuiltEnv, Exposure, Chemistry, Manufacture, Characterization, and Geospatial. Under each module, there are multiple sub-modules such as FTIR, AFM, Chem-Rxn, PV-Module, Accelerated Exposure, etc. - SDLE Research Center + + LIFO + Life Ontology (LifO) + The Life Ontology (LifO) is an ontology of the life of organism. LifO represents the life processes of organisms and related entities and relations. LifO is a general purpose ontology that covers the common features associated with different organisms such as unicellular prokaryotes (e.g., E. coli) and multicellular organisms (e.g., human). + Yongqun "Oliver" He (YH) OWL - 2026-02-03 - CC BY-SA 4.0 - https://cwrusdle.bitbucket.io/files/MDS_Onto/index-en.html - Materials Science and Engineering - Materials Data Science - 0.3.1.16 + March 11, 2018 + Creative Commons 4.0 + https://bioportal.bioontology.org/ontologies/LIFO + Biology and Life Sciences + General Purpose + 1.0.17 - - IOF - Industrial Ontology Foundry (IOF) - The IOF Core Ontology contains notions found to be common across multiple manufacturing domains. This file is an RDF implementation of these notions. The ontology utilizes the Basic Formal Ontology or BFO as a top-level ontology but also borrows terms from various domain-independent or mid-level ontologies. The purpose of the ontology is to serve as a foundation for ensuring consistency and interoperability across various domain-specific reference ontologies the IOF publishes. - IOF Core Working Group + + FRAPO + Funding, Research Administration and Projects Ontology (FRAPO) + The Funding, Research Administration and Projects Ontology (FRAPO) is an ontology for describing the administrative information of research projects, e.g., grant applications, funding bodies, project partners, etc. + David Shotton RDF - 2020 - MIT - https://oagi.org/pages/Released-Ontologies - Industry - Manufacturing - 1.0 + Creative Commons 4.0 + http://www.sparontologies.net/ontologies/frapo + Scholarly Knowledge + Administration - - OntoKin - Chemical Kinetics Ontology (OntoKin) - OntoKin is an ontology developed for representing chemical kinetic reaction mechanisms. - IEEE + + DBpedia + DBpedia Ontology (DBpedia) + The DBpedia ontology is generated from the manually created specifications in the DBpedia Mappings Wiki. Each release of this ontology corresponds to a new release of the DBpedia dataset, which contains instance data extracted from various language versions of Wikipedia. The DBpedia ontology has evolved into a crowd-sourced effort, resulting in a shallow cross-domain ontology. + DBpedia Maintainers and Contributors OWL - 08 February 2022 + 2008-11-17 + Creative Commons 3.0 + https://wiki.dbpedia.org/ + General Knowledge + Knowledge Graph + + + CiTO + Citation Typing Ontology (CiTO) + The Citation Typing Ontology (CiTO) is an ontology that enables characterization of the nature or type of citations, both factually and rhetorically. + Silvio Peroni, David Shotton + OWL + 2018-02-16 Creative Commons 4.0 - https://www.ontologyportal.org/ - Chemistry - 1.0 + https://github.com/SPAROntologies/cito/tree/master/docs/current + Scholarly Knowledge + Scholarly Communication + 2.8.1 - - Contact - Contact Ontology (Contact) - Ontology to capture concepts related to contact information (addresses, phone numbers). Reuses the iContact Ontology developed by the Enterprise Integration Lab in Toronto. The iContact ontology is extended to introduce a specialized definition of Hours of Operation, defined as a subclass of both the iContact definition of hours of operation, and a subclass of the Recurring Event class defined in the iCity Recurring Event ontology. The Contact ontology also extends the definition of address to include an associated location. - Mark Fox, Megan Katsumi - RDF - 2018-07-06 - https://enterpriseintegrationlab.github.io/icity/Contact/Contact_1.0/doc/index-en.html - Social Sciences - Social - 1.0 + + EXPO + Ontology of Scientific Experiments (EXPO) + Formalise generic knowledge about scientific experimental design, methodology, and results representation. + OWL + Academic Free License (AFL) + https://expo.sourceforge.net/ + Scholarly Knowledge + Scientific Experiments SchemaOrg @@ -467,154 +451,145 @@ Web Development 28.1 - - PTO - Product Types Ontology (PTO) - The Product Types Ontology is designed to be used in combination with GoodRelations, a standard vocabulary for the commercial aspects of offers. - Martin Hepp + + Framester + Framester Ontology (Framester) + Framester is a a frame-based ontological resource acting as a hub between linguistic resources such as FrameNet, WordNet, VerbNet, BabelNet, DBpedia, Yago, DOLCE-Zero, and leveraging this wealth of links to create an interoperable predicate space formalized according to frame semantics and semiotics. Framester uses WordNet and FrameNet at its core, expands it to other resources transitively, and represents them in a formal version of frame semantics. + Aldo Gangemi RDF - 2025-02-21 - Creative Commons 3.0 - http://www.productontology.org/ - Industry + 19-04-2016 + Creative Commons 4.0 + http://150.146.207.114/lode/extract?url=http://ontologydesignpatterns.org/ont/framester/framester.owl + Scholarly Knowledge + Linguistics 1.0 - - OM - Ontology of Units of Measure (OM) - The Ontology of units of Measure (OM) models concepts and relations important to scientific research. It has a strong focus on units, quantities, measurements, and dimensions. It includes, for instance, common units such as the SI units metre and kilogram, but also units from other systems of units such as the mile or nautical mile. For many application areas it includes more specific units and quantities, such as the unit of the Hubble constant or the quantity vaselife. The following application areas are supported by OM: Geometry; Mechanics; Thermodynamics; Electromagnetism; Fluid mechanics; Chemical physics; Photometry; Radiometry and Radiobiology; Nuclear physics; Astronomy and Astrophysics; Cosmology; Earth science; Meteorology; Material science; Microbiology; Economics; Information technology and Typography. - Hajo Rijgersberg, Don Willems, Jan Top + + MatVoc + Materials Vocabulary (MatVoc) + The official ontology produced in the context of the STREAM project. + Tatyana Sheveleva, Javad Chamanara RDF - June 28, 2024 + 2022-12-12 + MIT License + https://stream-project.github.io/#overv + Materials Science and Engineering + Materials Science + 1.0.0 + + + BIO + BIO: A vocabulary for biographical information (BIO) + The BIO vocabulary contains terms useful for finding out more about people and their backgrounds and has some cross-over into genealogical information. The approach taken is to describe a person's life as a series of interconnected key events, around which other information can be woven. This vocabulary defines the event framework and supplies a set of core event types that cover many use cases, but it is expected that it will be extended in other vocabularies to suit their needs. The intention of this vocabulary is to describe biographical events of people and this intention carries through to the definitions of the properties and classes which are person-centric rather than neutral. For example the Employment event puts the person being employed as the principal agent in the event rather than the employer. + Ian Davis and David Galbraith + RDF + 2010-05-10 + Public Domain + https://vocab.org/bio/ + Social Sciences + Biographical Information + 0.1 + + + BBCFood + BBC Food Ontology (BBCFood) + The Food Ontology is a simple lightweight ontology for publishing data about recipes, including the foods they are made from and the foods they create as well as the diets, menus, seasons, courses and occasions they may be suitable for. Whilst it originates in a specific BBC use case, the Food Ontology should be applicable to a wide range of recipe data publishing across the web. + TTL + 2014/03/18 Creative Commons 4.0 - https://bioportal.bioontology.org/ontologies/OM - Units and Measurements - 2.0.57 + https://www.bbc.co.uk/ontologies/food-ontology + News and Media + Food and Beverage + 0.1 - - OIEMaterials - Open Innovation Environment Materials (OIEMaterials) - The materials module populates the physicalistic perspective with materials subclasses categorised according to modern applied physical sciences. - Adham Hashibon, Daniele Toti, Emanuele Ghedini, Georg J. Schmitz, Gerhard Goldbeck, Jesper Friis, Pierluigi Del Nostro + + SSN + Semantic Sensor Network Ontology (SSN) + The Semantic Sensor Network (SSN) ontology is an ontology for describing sensors and their observations, the involved procedures, the studied features of interest, the samples used to do so, and the observed properties, as well as actuators. SSN follows a horizontal and vertical modularization architecture by including a lightweight but self-contained core ontology called SOSA (Sensor, Observation, Sample, and Actuator) for its elementary classes and properties. With their different scope and different degrees of axiomatization, SSN and SOSA are able to support a wide range of applications and use cases, including satellite imagery, large-scale scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Web of Things. Both ontologies are described below, and examples of their usage are given. + W3C/OGC Spatial Data on the Web Working Group TTL - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/emmo-repo/OIE-Ontologies/ + 2017-04-17 + http://www.w3.org/Consortium/Legal/2015/copyright-software-and-document + https://github.com/w3c/sdw-sosa-ssn/tree/482484fe2edc1ba8aa7f19214a72bdb77123e833 Materials Science and Engineering - Materials + Sensor Networks + 1.0 - - NCIt - NCI Thesaurus (NCIt) - NCI Thesaurus (NCIt) is a reference terminology that includes broad coverage of the cancer domain, including cancer related diseases, findings and abnormalities. The NCIt OBO Edition aims to increase integration of the NCIt with OBO Library ontologies. NCIt OBO Edition releases should be considered experimental. - OWL - 2023-10-19 + + Hydra + Hydra Ontology (Hydra) + Hydra is a lightweight vocabulary to create hypermedia-driven Web APIs. By specifying a number of concepts commonly used in Web APIs it enables the creation of generic API clients. + Hydra W3C Community Group + JSONLD + 13 July 2021 Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/NCIT - Medicine - Cancer, Oncology - 24.04e + https://www.hydra-cg.com/spec/latest/core/#references + Web and Internet + Web Development - - DBO - Digital Buildings Ontology (DBO) - The Digital Buildings ontology (DBO) is used by Google to represent structured information about buildings and building-installed equipment. - Google + + IOF + Industrial Ontology Foundry (IOF) + The IOF Core Ontology contains notions found to be common across multiple manufacturing domains. This file is an RDF implementation of these notions. The ontology utilizes the Basic Formal Ontology or BFO as a top-level ontology but also borrows terms from various domain-independent or mid-level ontologies. The purpose of the ontology is to serve as a foundation for ensuring consistency and interoperability across various domain-specific reference ontologies the IOF publishes. + IOF Core Working Group RDF - 02/23/2023 - Apache 2.0 - https://github.com/google/digitalbuildings?tab=readme-ov-file + 2020 + MIT + https://oagi.org/pages/Released-Ontologies Industry - Building Information - 0.0.1 - - - PROV - PROV Ontology (PROV-O) - The PROV Ontology (PROV-O) expresses the PROV Data Model [PROV-DM] using the OWL2 Web Ontology Language (OWL2) [OWL2-OVERVIEW]. It provides a set of classes, properties, and restrictions that can be used to represent and interchange provenance information generated in different systems and under different contexts. It can also be specialized to create new classes and properties to model provenance information for different applications and domains. The PROV Document Overview describes the overall state of PROV, and should be read before other PROV documents. - OWL - 2013-04-30 - W3C Software License - https://terminology.tib.eu/ts/ontologies/PROV - General Knowledge - General - 2013-04-30 - - - OEO - The Open Energy Ontology (OEO) - The Open Energy Ontology (OEO) is a domain ontology of the energy system analysis context. It is developed as part of the Open Energy Family. The OEO is published on GitHub under an open source license. The language used is the Manchester OWL Syntax, which was chosen because it is user-friendly for editing and viewing differences of edited files. The OEO is constantly being extended. The first version of the OEO has been released on June 11th 2020. A Steering Committee (OEO-SC) was created to accompany the development, increase awareness of the ontology and include it in current projects. - OWL - 03/2025 - Creative Commons Attribution 1.0 Generic (CC BY 1.0) - https://github.com/OpenEnergyPlatform/ontology?tab=readme-ov-file - Ecology and Environment - Energy - 2.7.0 + Manufacturing + 1.0 - - BBC - BBC Ontology (BBC) - The BBC ontology codifies the logic that connects web documents, BBC products and platforms for which content is available. Currently, there are 10 major products in Future Media which produce content for BBC online. The majority of those contain more products dedicated in thematic areas, for example Education propositions are part of the K&L (Knowledge and Learning) product portfolio. - LinkedData@bbc.co.uk + + BBCStoryline + BBC Storyline Ontology (BBCStoryline) + The News Storyline Ontology is a generic model for describing and organising the stories news organisations tell. The ontology is intended to be flexible to support any given news or media publisher's approach to handling news stories. At the heart of the ontology, is the concept of Storyline. As a nuance of the English language the word 'story' has multiple meanings. In news organisations, a story can be an individual piece of content, such as an article or news report. It can also be the editorial view on events occurring in the world. + http://uk.linkedin.com/in/paulwilton, http://www.bbc.co.uk/blogs/internet/authors/Jeremy_Tarling, http://uk.linkedin.com/in/jarredmcginnis TTL - 2012-12-01 + 2013-05-01 Creative Commons 4.0 - https://www.bbc.co.uk/ontologies/bbc-ontology/ + https://iptc.org/thirdparty/bbc-ontologies/storyline.html News and Media - News - 1.37 - - - VIBSO - Vibrational Spectroscopy Ontology (VIBSO) - The Vibration Spectroscopy Ontology defines technical terms with which research data produced in vibrational spectroscopy experiments can be semantically enriched, made machine readable and FAIR. - VIBSO Workgroup - OWL - 2024-09-23 - Creative Commons Attribution 4.0 - https://terminology.tib.eu/ts/ontologies/vibso - Chemistry - Spectroscopy - 2024-09-23 + Storyline + 0.3 - - BBCCreativeWork - BBC Creative Work Ontology (BBCCreativeWork) - This ontology defines the terms required to describe the creative works produced by the BBC and their associated metadata. This ontology powers reading and writing creative works in the triplestore using tags associated with them (about) their more specific types (BlogPost, NewsItem, Programme) and audiences (audience). - LinkedData@bbc.co.uk + + BBCProgrammes + BBC Programmes Ontology (BBCProgrammes) + This ontology aims at providing a simple vocabulary for describing programmes. It covers brands, series (seasons), episodes, broadcast events, broadcast services,etc. Its development was funded by the BBC, and is heavily grounded on previous programmes data modelling work done there. + https://moustaki.org/foaf.rdf#moustaki TTL - 2012-12-01 + 2009/02/20 Creative Commons 4.0 - https://www.bbc.co.uk/ontologies/creative-work-ontology + https://www.bbc.co.uk/ontologies/programmes-ontology News and Media - Creative Work - 1.19 - - - NFDIcore - National Research Data Infrastructure Ontology (NFDIcore) - The National Research Data Infrastructure (NFDI) initiative has led to the formation of various consortia, each focused on developing a research data infrastructure tailored to its specific domain. To ensure interoperability across these consortia, the NFDIcore ontology has been developed as a mid-level ontology for representing metadata related to NFDI resources, including individuals, organizations, projects, data portals, and more. - Jörg Waitelonis, Oleksandra Bruns, Tabea Tietz, Etienne Posthumus, Hossein Beygi Nasrabadi, Harald Sack - OWL - 2025-02-07 - Creative Commons 1.0 - https://ise-fizkarlsruhe.github.io/nfdicore/ - Scholarly Knowledge - Research Data Infrastructure - 3.0.0 + Programmes + 1.1 - - PreMOn - Pre-Modern Ontology (PreMOn) - The PreMOn Ontology is an extension of lemon (W3C Ontology Lexicon Community Group, 2015) for representing predicate models and their mappings. The Core Module of the PreMOn Ontology defines the main abstractions for modelling semantic classes with their semantic roles, mappings between different predicate models, and annotations. - Francesco Corcoglioniti, Marco Rospocher <https://dkm.fbk.eu/rospocher> + + GEO + Geographical Entities Ontology (GEO) + Geographical Entities Ontology (GEO) is an inventory of geopolitical entities (such as sovereign states and their administrative subdivisions) as well as various geographical regions (including but not limited to the specific ones over which the governments have jurisdiction) + William R Hogan OWL - 2018-02-15 + 2019-02-17 Creative Commons 4.0 - https://premon.fbk.eu/ontology/core# - Scholarly Knowledge - Linguistics - 2018a + https://github.com/mcwdsi/geographical-entity-ontology/blob/master/geo-all.owl + Geography + Geographic Knowledge + + + DBO + Digital Buildings Ontology (DBO) + The Digital Buildings ontology (DBO) is used by Google to represent structured information about buildings and building-installed equipment. + Google + RDF + 02/23/2023 + Apache 2.0 + https://github.com/google/digitalbuildings?tab=readme-ov-file + Industry + Building Information + 0.0.1 ASMO @@ -628,328 +603,327 @@ Materials Science 1.0.0 - - DISO - Dislocation Ontology (DISO) - DISO is an ontology that defines the linear defect, in particular dislocation concepts and relations between them in crystalline materials. - Ahmad Zainul Ihsan - OWL - 21.03.202 - Creative Commons Attribution 3.0 International (CC BY 3.0) - https://github.com/Materials-Data-Science-and-Informatics/dislocation-ontology - Materials Science and Engineering - Materials Science - 1.0 - - - NanoMine - NanoMine Ontology (NanoMine) - Polymer Nanocomposites based ontology which enable researchers to develop and test broad-reaching hypotheses about how inter-relationships between different materials processing methods and composition result in specific changes in material properties. - TTL - APACHE 2.0 - https://github.com/tetherless-world/nanomine-ontology - Materials Science and Engineering - Materials Science - - - BTO - BRENDA Tissue Ontology (BTO) - A structured controlled vocabulary for the source of an enzyme comprising tissues, cell lines, cell types and cell cultures. + + GFO + General Formal Ontology (GFO) + The General Formal Ontology is a top-level ontology for conceptual modeling, which is being constantly further developed by Onto-Med. It includes elaborations of categories like objects, processes, time and space, properties, relations, roles, functions, facts, and situations. Moreover, we are working on an integration with the notion of levels of reality in order to more appropriately capture entities in the material, mental, and social areas. OWL - 2021-10-26 + 2024-11-18 Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/BTO - Medicine - Enzyme - 2021-10-26 + https://onto-med.github.io/GFO/release/2024-11-18/index-en.html + Upper Ontology - - GEO - Geographical Entities Ontology (GEO) - Geographical Entities Ontology (GEO) is an inventory of geopolitical entities (such as sovereign states and their administrative subdivisions) as well as various geographical regions (including but not limited to the specific ones over which the governments have jurisdiction) - William R Hogan + + SWEET + Semantic Web for Earth and Environment Technology Ontology (SWEET) + The Semantic Web for Earth and Environment Technology Ontology (SWEET) is an investigation in improving discovery and use of Earth science data, through software understanding of the semantics of web resources. SWEET is a collection of ontologies conceptualizing a knowledge space for Earth system science, represented using the web ontology language (OWL). It includes both orthogonal concepts (space, time, Earth realms, physical quantities, etc.) and integrative science knowledge concepts (phenomena, events, etc.). + NASA, JPL, Caltech OWL - 2019-02-17 + July 14, 2022 Creative Commons 4.0 - https://github.com/mcwdsi/geographical-entity-ontology/blob/master/geo-all.owl - Geography - Geographic Knowledge + https://bioportal.bioontology.org/ontologies/SWEET + Ecology and Environment + Earth Science, Geoscience + 3.6.0 - - Hydra - Hydra Ontology (Hydra) - Hydra is a lightweight vocabulary to create hypermedia-driven Web APIs. By specifying a number of concepts commonly used in Web APIs it enables the creation of generic API clients. - Hydra W3C Community Group - JSONLD - 13 July 2021 + + PATO + Phenotype and Trait Ontology (PATO) + An ontology of phenotypic qualities (properties, attributes or characteristics). + OWL + 2025-02-01 Creative Commons 4.0 - https://www.hydra-cg.com/spec/latest/core/#references - Web and Internet - Web Development + https://terminology.tib.eu/ts/ontologies/PATO + Biology and Life Sciences + Biology + 1.2 - - MOLTENSILE - Matolab Tensile Test Ontology (MOL_TENSILE) - An ontology for describing the tensile test process, made in the Materials Open Lab Project. - Markus Schilling, markus.schilling@bam.de; Philipp von Hartrott, philipp.von.hartrott@iwm.fraunhofer.de + + Common + Common Ontology (Common) + Ontology for the representation of commons elements in the Trias ontology + Jhon Toledo, Miguel Angel García, Oscar Corcho RDF - 04/16/2021 Creative Commons Attribution 4.0 International (CC BY 4.0) - https://matportal.org/ontologies/MOL_TENSILE - Materials Science and Engineering - Materials Testings - 0.4 - - - MOP - Molecular Process Ontology (MOP) - MOP is the molecular process ontology. It contains the molecular processes that underlie the name reaction ontology RXNO, for example cyclization, methylation and demethylation. + https://w3id.org/mobility/trias/common/0.1.0 + Education + Computer Science + 0.1.0 + + + PTO + Product Types Ontology (PTO) + The Product Types Ontology is designed to be used in combination with GoodRelations, a standard vocabulary for the commercial aspects of offers. + Martin Hepp + RDF + 2025-02-21 + Creative Commons 3.0 + http://www.productontology.org/ + Industry + 1.0 + + + CHIRO + CHEBI Integrated Role Ontology (CHIRO) + CHEBI provides a distinct role hierarchy. Chemicals in the structural hierarchy are connected via a 'has role' relation. CHIRO provides links from these roles to useful other classes in other ontologies. This will allow direct connection between chemical structures (small molecules, drugs) and what they do. This could be formalized using 'capable of', in the same way Uberon and the Cell Ontology link structures to processes. OWL - 2022-05-11 - Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/MOP + 2015-11-23 + Creative Commons 1.0 + https://terminology.tib.eu/ts/ontologies/chiro Chemistry - Chemistry, Molecular Biology - 2022-05-11 + Chemicals, Roles + 2015-11-23 - - Atomistic - Atomistic Ontology (Atomistic) - An EMMO-based domain ontology for atomistic and electronic modelling. - Francesca L. Bleken, Jesper Friis + + BBCPolitics + BBC Politics News Ontology (BBCPolitics) + The Politics Ontology describes the concepts that occur in BBC politics news. + https://www.r4isstatic.com/ + TTL + 2014-01-06 + Creative Commons 4.0 + https://www.bbc.co.uk/ontologies/politics-ontology + News and Media + Politics + 0.9 + + + BVCO + Battery Value Chain Ontology (BVCO) + Basically, Battery Value Chain Ontology (BVCO) aims to model processes along the Battery value chain. Processes are holistic perspective elements that transform inputs/educts (matter, energy, information) into output/products (matter, energy, information) with the help of tools (devices, algorithms). They can be decomposed into sub-processes and have predecessor and successor processes. + Lukas Gold, Simon Stier TTL Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/emmo-repo/domain-atomistic + https://github.com/Battery-Value-Chain-Ontology/ontology Materials Science and Engineering Materials Science - 0.0.2 + 0.4.3 - - GND - Gemeinsame Normdatei (GND) - GND stands for Gemeinsame Normdatei (Integrated Authority File) and offers a broad range of elements to describe authorities. The GND originates from the German library community and aims to solve the name ambiguity problem in the library world. - Alexander Haffner + + SUMO + Suggested Upper Merged Ontology (SUMO) + The Suggested Upper Merged Ontology (SUMO) and its domain ontologies form the largest formal public ontology in existence today. They are being used for research and applications in search, linguistics and reasoning. SUMO is the only formal ontology that has been mapped to all of the WordNet lexicon. + OWL + 2025-02-17 + Creative Commons 4.0 + https://www.ontologyportal.org/ + Upper Ontology + 1.0 + + + DublinCore + Dublin Core Vocabulary (DublinCore) + The Dublin Core Schema is a small set of vocabulary terms that can be used to describe several kinds of resources. Dublin Core Metadata may be used for multiple purposes, from simple resource description, to combining metadata vocabularies of different metadata standards, to providing interoperability for metadata vocabularies in the Linked Data cloud and Semantic Web implementations. + The Dublin Core Metadata Initiative RDF - 2024-08-26 - Creative Commons 1.0 - https://d-nb.info/standards/elementset/gnd - Library and Cultural Heritage - Authority Files - 1.2.0 + February 17, 2017 + Public Domain + https://bioportal.bioontology.org/ontologies/DC + General Knowledge + Metadata + 1.1 - - OIEModels - Open Innovation Environment Models (OIEModels) - The models module defines models as semiotic signs that stands for an object by resembling or imitating it, in shape or by sharing a similar logical structure. - Adham Hashibon, Daniele Toti, Emanuele Ghedini, Georg J. Schmitz, Gerhard Goldbeck, Jesper Friis, Pierluigi Del Nostro - TTL + + MOLTENSILE + Matolab Tensile Test Ontology (MOL_TENSILE) + An ontology for describing the tensile test process, made in the Materials Open Lab Project. + Markus Schilling, markus.schilling@bam.de; Philipp von Hartrott, philipp.von.hartrott@iwm.fraunhofer.de + RDF + 04/16/2021 Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/emmo-repo/OIE-Ontologies/ + https://matportal.org/ontologies/MOL_TENSILE Materials Science and Engineering - Materials + Materials Testings + 0.4 - - EMMOCrystallography - Crystallography Ontology (EMMOCrystallography) - A crystallography domain ontology based on EMMO and the CIF core dictionary. It is implemented as a formal language. - TTL - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/emmo-repo/domain-crystallography - Materials Science and Engineering - Crystallography - 0.0.1 + + BTO + BRENDA Tissue Ontology (BTO) + A structured controlled vocabulary for the source of an enzyme comprising tissues, cell lines, cell types and cell cultures. + OWL + 2021-10-26 + Creative Commons 4.0 + https://terminology.tib.eu/ts/ontologies/BTO + Medicine + Enzyme + 2021-10-26 - - OWLTime - Time Ontology in OWL (OWL-Time) - OWL-Time is an OWL-2 DL ontology of temporal concepts, for describing the temporal properties of resources in the world or described in Web pages. The ontology provides a vocabulary for expressing facts about topological (ordering) relations among instants and intervals, together with information about durations, and about temporal position including date-time information. Time positions and durations may be expressed using either the conventional (Gregorian) calendar and clock, or using another temporal reference system such as Unix-time, geologic time, or different calendars. - World Wide Web Consortium - TTL - 15 November 2022 - W3C Software Notice and Document License - https://www.w3.org/TR/owl-time/ - Units and Measurements - Temporal Reasoning + + TimelineOntology + Timeline Ontology (TimelineOntology) + The Timeline Ontology is centered around the notion of timeline, seen here as a way to identify a temporal backbone. A timeline may support a signal, a video, a score, a work, etc. + Christopher Sutton, Yves Raimond, Matthias Mauch + RDF + 25th October 2007 + Creative Commons 1.0 + https://github.com/motools/timelineontology + Arts and Humanities + Music Theory 1.0 - - MSLE - Material Science Lab Equipment Ontology (MSLE) - The current ontology describes Material Science Lab Equipment. - TTL - Sep 15, 2022 - https://github.com/MehrdadJalali-AI/MSLE-Ontology - Materials Science and Engineering - Materials Science - 1.1 - - - MatOnto - Material Ontology (MatOnto) - The Material Ontology (MatOnto) is based on the upper level ontology, the BFO. - OWL - https://github.com/EngyNasr/MSE-Benchmark/blob/main/testCases/secondTestCase/MatOnto.owl - Materials Science and Engineering + + LexInfo + LexInfo (LexInfo) + LexInfo allows us to associate linguistic information to elements in an ontology with respect to any level of linguistic description and expressivity. LexInfo has been implemented as an OWL ontology and is available together with an API. + RDF + Apache 2.0 + https://lexinfo.net/index.html Scholarly Knowledge + Linguistics + 3.0 - - SPDocument - SMART Protocols Ontology: Document Module (SP-Document) - SMART Protocols Ontology: Document Module is an ontology designed to represent metadata used to report an experimental protocol. - http://oxgiraldo.wordpress.com + + OPMW + Open Provenance Model for Workflows (OPMW) + The Open Provenance Model for Workflows (OPMW) is an ontology for describing workflow traces and their templates based on the Open Provenance Model. It has been designed as a profile for OPM, extending and reusing OPM's core ontologies OPMV (OPM-Vocabulary) and OPMO (OPM-Ontology). + http://delicias.dia.fi.upm.es/members/DGarijo/#me, http://www.isi.edu/~gil/ OWL - 2013-07-01 - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/SMARTProtocols/SMART-Protocols + 2014-12-22 + Creative Commons Attribution 2.0 Generic (CC BY 2.0) + https://www.opmw.org/model/OPMW_20141222/ Scholarly Knowledge - Materials Science - 4.0 + Workflows + 3.1 - - DBpedia - DBpedia Ontology (DBpedia) - The DBpedia ontology is generated from the manually created specifications in the DBpedia Mappings Wiki. Each release of this ontology corresponds to a new release of the DBpedia dataset, which contains instance data extracted from various language versions of Wikipedia. The DBpedia ontology has evolved into a crowd-sourced effort, resulting in a shallow cross-domain ontology. - DBpedia Maintainers and Contributors + + MatWerk + NFDI MatWerk Ontology (MatWerk) + NFDI-MatWerk aims to establish a digital infrastructure for Materials Science and Engineering (MSE), fostering improved data sharing and collaboration. This repository provides comprehensive documentation for NFDI MatWerk Ontology (MWO) v3.0, a foundational framework designed to structure research data and enhance interoperability within the MSE community. To ensure compliance with top-level ontology standards, MWO v3.0 is aligned with the Basic Formal Ontology (BFO) and incorporates the modular approach of the NFDIcore mid-level ontology, enriching metadata through standardized classes and properties. The MWO addresses key aspects of MSE research data, including the NFDI-MatWerk community structure, covering task areas, infrastructure use cases, projects, researchers, and organizations. It also describes essential NFDI resources, such as software, workflows, ontologies, publications, datasets, metadata schemas, instruments, facilities, and educational materials. Additionally, MWO represents NFDI-MatWerk services, academic events, courses, and international collaborations. As the foundation for the MSE Knowledge Graph, MWO facilitates efficient data integration and retrieval, promoting collaboration and knowledge representation across MSE domains. This digital transformation enhances data discoverability, reusability, and accelerates scientific exchange, innovation, and discoveries by optimizing research data management and accessibility. + Hossein Beygi Nasrabadi, Jörg Waitelonis, Ebrahim Norouzi, Kostiantyn Hubaiev, Harald Sack + TTL + 2025-03-01 + Creative Commons 1.0 + https://github.com/ISE-FIZKarlsruhe/mwo?tab=readme-ov-file + Materials Science and Engineering + Research Data, Interoperability + 3.0.0 + + + UO + Units of Measurement Ontology (UO) + Metrical units for use in conjunction with PATO. + KAUST OWL - 2008-11-17 + 2023-05-25 Creative Commons 3.0 - https://wiki.dbpedia.org/ - General Knowledge - Knowledge Graph + https://bioportal.bioontology.org/ontologies/UO + Units and Measurements - - Metadata4Ing - Metadata for Intelligent Engineering (Metadata4Ing) - The ontology Metadata4Ing provides a framework for the semantic description of research data and of the whole data generation process, embracing the object of investigation, all sample and data manipulation methods and tools, the data files themselves, and the roles of persons and institutions. The structure and application of the ontology are based on the principles of modularity and inheritance. - Metadata4Ing Workgroup - TTL - 2025-03-10 + + MDO + Materials Design Ontology (MDO) + MDO is an ontology for materials design field, representing the domain knowledge specifically related to solid-state physics and computational materials science. + Materials Design Division, National Institute for Materials Science (NIMS) + OWL + 2022-08-02 Creative Commons 4.0 - https://git.rwth-aachen.de/nfdi4ing/metadata4ing/metadata4ing - Scholarly Knowledge - Materials Science - 1.3.1 + https://github.com/LiUSemWeb/Materials-Design-Ontology/tree/master/ + Materials Science and Engineering + Materials Design + 1.1 - - iCalendar - iCalendar Vocabulary (iCalendar) - iCalendar is an Internet standard for exchanging calendar and scheduling data across different applications and platforms using a standardized text-based format (.ics). It enables interoperability for events, tasks, and scheduling, supporting features like recurring events, invitations, and time zone adjustments. While widely used in applications like Google Calendar and Outlook, its complexity and partial implementations pose challenges, leading to efforts to integrate it with Semantic Web technologies for enhanced data linking and automation. - Dan Connolly, W3C, Libby Miller, ASemantics - RDF - 2004/04/07 - Open Publication License - https://www.w3.org/2002/12/cal/ - Events - Calendar and Scheduling - 1.14 - - - Wine - Wine Ontology (Wine) - A project to define an RDF style ontology for wines and the wine-industry + + AGROVOC + AGROVOC Multilingual Thesaurus (AGROVOC) + AGROVOC is a relevant Linked Open Data set about agriculture available for public use and facilitates access and visibility of data across domains and languages. It offers a structured collection of agricultural concepts, terms, definitions and relationships which are used to unambiguously identify resources, allowing standardized indexing processes and making searches more efficient. + Food and Agriculture Organization of the United Nations RDF - https://github.com/UCDavisLibrary/wine-ontology - Food and Beverage - Wine - - - PeriodicTable - Periodic Table of the Elements Ontology (PeriodicTable) - PeriodicTable.owl is a representation of the Periodic Table of the Elements in the OWL Web Ontology Language. It provides reference data to support Semantic Web applications in chemistry and related disciplines. - Michael Cook - OWL - 2004/02/05 - https://www.daml.org/2003/01/periodictable/ - Materials Science and Engineering - Periodic Table of Elements - 1.10 + August 12, 2024 + Creative Commons 4.0 + https://agroportal.lirmm.fr/ontologies/AGROVOC + Agriculture + Agricultural Knowledge + 2024-04 - - MarineTLO - Marine Taxonomy and Life Ontology (MarineTLO) - MarineTLO is a top level ontology, generic enough to provide consistent abstractions or specifications of concepts included in all data models or ontologies of marine data sources and provide the necessary properties to make this distributed knowledge base a coherent source of facts relating observational data with the respective spatiotemporal context and categorical (systematic) domain knowledge. It can be used as the core schema for publishing Linked Data, as well as for setting up integration systems for the marine domain. It can be extended to any level of detail on demand, while preserving monotonicity. For its development and evolution we have adopted an iterative and incremental methodology where a new version is released every two months. For the implementation we use OWL 2, and to evaluate it we use a set of competency queries, formulating the domain requirements provided by the related communities. - Information System Laboratory (ISL), Institute of Computer Science (ICS), Foundation for Research and Technology - Hellas (FORTH) + + MGED + MGED Ontology (MGED) + An ontology for microarray experiments in support of MAGE v.1. Concepts, definitions, terms, and resources for standardized description of a microarray experiment in support of MAGE v.1. The MGED ontology is divided into the MGED Core ontology which is intended to be stable and in synch with MAGE v.1; and the MGED Extended ontology which adds further associations and classes not found in MAGE v.1 + Chris Stoeckert, Helen Parkinson, Trish Whetzel, Paul Spellman, Catherine A. Ball, Joseph White, John Matese, Liju Fan, Gilberto Fragoso, Mervi Heiskanen, Susanna Sansone, Helen Causton, Laurence Game, Chris Taylor OWL - 2017-01-05 + Feb. 9, 2007 Creative Commons 4.0 - https://projects.ics.forth.gr/isl/MarineTLO/ + https://mged.sourceforge.net/ontologies/MGEDontology.php/ Biology and Life Sciences - Marine Science, Oceanography - 1.0 - - - VOAF - Vocabulary of a Friend (VOAF) - The Vocabulary of a Friend (VOAF) is a vocabulary specification providing elements allowing the description of vocabularies (RDFS vocabularies or OWL ontologies). It is based on Dublin Core and VOID. - Bernard Vatant - RDF - 2013-05-24 - Creative Commons 3.0 - https://lov.linkeddata.es/vocommons/voaf/v2.3/ - Scholarly Knowledge - Social Network - 2.3 + Domain Ontology + 1.3.1.1 - - QUDT - Quantities, Units, Dimensions and Data Types (QUDT) - QUDT is an advocate for the development and implementation of standards to quantify data expressed in RDF and JSON. - NASA Ames Research Center + + BBCCMS + BBC CMS Ontology (BBCCMS) + The Content Management Systems ontology defines the terms that LDP needs to interact with systems that produce content. The linked data platform contain semantic metadata for the creative content and also the things the BBC produces content about. The CMS ontology defines how these things and content are associated with other BBC instances of the same thing. + LinkedData@bbc.co.uk TTL - March 1, 2022 + 2012-12-01 Creative Commons 4.0 - https://qudt.org/ - Units and Measurements - Physics - 2.1 + https://www.bbc.co.uk/ontologies/cms-ontology + News and Media + Content Management Systems + 3.7 - - BIO - BIO: A vocabulary for biographical information (BIO) - The BIO vocabulary contains terms useful for finding out more about people and their backgrounds and has some cross-over into genealogical information. The approach taken is to describe a person's life as a series of interconnected key events, around which other information can be woven. This vocabulary defines the event framework and supplies a set of core event types that cover many use cases, but it is expected that it will be extended in other vocabularies to suit their needs. The intention of this vocabulary is to describe biographical events of people and this intention carries through to the definitions of the properties and classes which are person-centric rather than neutral. For example the Employment event puts the person being employed as the principal agent in the event rather than the employer. - Ian Davis and David Galbraith - RDF - 2010-05-10 - Public Domain - https://vocab.org/bio/ - Social Sciences - Biographical Information - 0.1 + + GTS + Geologic Timescale model (GTS) + This is an RDF/OWL representation of the GeoSciML Geologic Timescale model, which has been adapted from the model described in Cox, S.J.D, & Richard, S.M. (2005) A formal model for the geologic timescale and GSSP, compatible with geospatial information transfer standards, Geosphere, Geological Society of America. + Simon J D Cox (simon.cox@csiro.au) of CSIRO + TTL + 2020-05-31 + Creative Commons 1.0 + https://raw.githack.com/CGI-IUGS/timescale-ont/master/html/gts.html + Geography + geospatial Information, Geology + 1.0 - - FAIR - FAIR Vocabulary (FAIR) - This is the formal vocabulary (ontology) describing the FAIR principles. + + NMRCV + Nuclear Magnetic Resonance Controlled Vocabulary (NMRCV) + This artefact is an MSI-approved controlled vocabulary primarily developed under COSMOS EU and PhenoMeNal EU governance. The nmrCV is supporting the nmrML XML format with standardized terms. nmrML is a vendor agnostic open access NMR raw data standard. Its primaly role is analogous to the mzCV for the PSI-approved mzML XML format. It uses BFO2.0 as its Top level. This CV was derived from two predecessors (The NMR CV from the David Wishart Group, developed by Joseph Cruz) and the MSI nmr CV developed by Daniel Schober at the EBI. This simple taxonomy of terms (no DL semantics used) serves the nuclear magnetic resonance markup language (nmrML) with meaningful descriptors to amend the nmrML xml file with CV terms. Metabolomics scientists are encouraged to use this CV to annotrate their raw and experimental context data, i.e. within nmrML. The approach to have an exchange syntax mixed of an xsd and CV stems from the PSI mzML effort. The reason to branch out from an xsd into a CV is, that in areas where the terminology is likely to change faster than the nmrML xsd could be updated and aligned, an externally and decentrallised maintained CV can accompensate for such dynamics in a more flexible way. A second reason for this set-up is that semantic validity of CV terms used in an nmrML XML instance (allowed CV terms, position/relation to each other, cardinality) can be validated by rule-based proprietary validators: By means of cardinality specifications and XPath expressions defined in an XML mapping file (an instances of the CvMappingRules.xsd ), one can define what ontology terms are allowed in a specific location of the data model. + Daniel Schober OWL + 2017-10-19 Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/FAIR - Upper Ontology - Data, Metadata + https://terminology.tib.eu/ts/ontologies/NMRCV + Chemistry + 1.1.0 - - ENM - Environmental Noise Measurement Ontology (ENM) - The eNanoMapper project (https://www.enanomapper.net/), NanoCommons project (https://www.nanocommons.eu/) and ACEnano project (http://acenano-project.eu/) are creating a pan-European computational infrastructure for toxicological data management for ENMs, based on semantic web standards and ontologies. This ontology is an application ontology targeting the full domain of nanomaterial safety assessment. It re-uses several other ontologies including the NPO, CHEMINF, ChEBI, and ENVO. - eNanoMapper Consortium - OWL - 2025-02-17 + + BBCBusiness + BBC Business News Ontology (BBCBusiness) + The Business News Ontology describes the concepts that occur in BBC business news. + https://www.bbc.co.uk/blogs/internet/authors/Jeremy_Tarling, https://uk.linkedin.com/in/amaalmohamed + TTL + 2014-11-09 + Creative Commons 4.0 + https://www.bbc.co.uk/ontologies/business-news-ontology + News and Media + Business News + 0.5 + + + YAGO + YAGO Ontology (YAGO) + YAGO is a large semantic knowledge base, derived from Wikipedia, WordNet, and GeoNames. It contains knowledge about more than 10 million entities and contains more than 120 million facts about these entities. YAGO is special in several ways: It has a clean taxonomy, which was manually built, and it is the only knowledge base with such a large coverage, the clean taxonomy, and the extraction from Wikipedia, WordNet, and GeoNames. + Max Planck Institute for Informatics + TTL + April, 2024 Creative Commons 3.0 - https://terminology.tib.eu/ts/ontologies/ENM - Medicine - Material Science and Engineering - 10.0 + https://yago-knowledge.org/downloads/yago-4-5 + General Knowledge + People, Cities, Countries, Movies, Organizations + 4.5 - - LDO - Line Defect Ontology (LDO) - LDO is an ontology designed to describe line defects in crystalline materials, such as dislocations and disclinations. - https://orcid.org/0000-0001-7564-7990 - OWL - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/OCDO/ldo - Materials Science and Engineering - Materials Defects - 1.0.0 + + Juso + Juso Ontology (Juso) + Juso Ontology is a Web vocabulary for describing geographical addresses and features. + James G. Kim, LiST Inc. + TTL + 2015-11-10 + Creative Commons 4.0 + https://rdfs.co/juso/0.1.1/html + Geography + geographical knowledge + 0.1.1 AS2 @@ -963,17 +937,30 @@ Social 2.0 - - BBCSport - BBC Sport Ontology (BBCSport) - The Sport Ontology is a simple lightweight ontology for publishing data about competitive sports events. The terms in this ontology allow data to be published about: The structure of sports tournaments as a series of eventsThe competing of agents in a competitionThe type of discipline a event involvesThe award associated with the competition and how received it...etc Whilst it originates in a specific BBC use case, the Sport Ontology should be applicable to a wide range of competitive sporting events data publishing use cases. Care has been taken to try and ensure interoperability with more general ontologies in use. In particular, it draws heavily upon the events ontology. - https://uk.linkedin.com/pub/jem-rayfield/27/b19/757, https://uk.linkedin.com/in/paulwilton, https://www.blockslabpillar.com, https://www.linkedin.com/in/tfgrahame, https://uk.linkedin.com/pub/stuart-williams/8/684/351, https://uk.linkedin.com/in/brianwmcbride - TTL - Creative Commons 4.0 - https://www.bbc.co.uk/ontologies/sport-ontology - News and Media - Sport - 3.2 + + PODO + Point Defects Ontology (PODO) + PODO focuses on the description of point defects in crystalline materials. + https://orcid.org/0000-0001-7564-7990 + OWL + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/OCDO/podo + Materials Science and Engineering + Materials Science + 1.0.0 + + + NFDIcore + National Research Data Infrastructure Ontology (NFDIcore) + The National Research Data Infrastructure (NFDI) initiative has led to the formation of various consortia, each focused on developing a research data infrastructure tailored to its specific domain. To ensure interoperability across these consortia, the NFDIcore ontology has been developed as a mid-level ontology for representing metadata related to NFDI resources, including individuals, organizations, projects, data portals, and more. + Jörg Waitelonis, Oleksandra Bruns, Tabea Tietz, Etienne Posthumus, Hossein Beygi Nasrabadi, Harald Sack + OWL + 2025-02-07 + Creative Commons 1.0 + https://ise-fizkarlsruhe.github.io/nfdicore/ + Scholarly Knowledge + Research Data Infrastructure + 3.0.0 MSEO @@ -986,41 +973,131 @@ Materials Science and Engineering Materials Science - - BVCO - Battery Value Chain Ontology (BVCO) - Basically, Battery Value Chain Ontology (BVCO) aims to model processes along the Battery value chain. Processes are holistic perspective elements that transform inputs/educts (matter, energy, information) into output/products (matter, energy, information) with the help of tools (devices, algorithms). They can be decomposed into sub-processes and have predecessor and successor processes. - Lukas Gold, Simon Stier + + DoCO + Document Components Ontology (DoCO) + DoCO, the Document Components Ontology, is an OWL 2 DL ontology that provides a general-purpose structured vocabulary of document elements. DoCO has been designed as a general unifying ontological framework for describing different aspects related to the content of scientific and other scholarly texts. Its primary goal has been to improve the interoperability and shareability of academic documents (and related services) when multiple formats are actually used for their storage. + David Shotton and Silvio Peroni + RDF + 2015-07-03 + Creative Commons 4.0 + http://www.sparontologies.net/ontologies/doco + Education + document components + 1.3 + + + CIFCore + Crystallographic Information Framework Core Dictionary (CIFCore) + (1) to explain the historical development of CIF dictionaries to define in a machine-actionable manner the contents of data files covering various aspects of crystallography and related structural sciences; (2) to demonstrate some of the more complex types of information that can be handled with this approach. TTL + May 24, 2023 Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/Battery-Value-Chain-Ontology/ontology + https://github.com/emmo-repo/CIF-ontology?tab=readme-ov-file Materials Science and Engineering Materials Science - 0.4.3 + 0.1.0 - - CMSO - Computational Material Sample Ontology (CMSO) - CMSO is an ontology that aims to describe computational materials science samples (or structures), including crystalline defects. Initially focusing on the description at the atomic scale. - https://orcid.org/0000-0001-7564-7990 + + iCalendar + iCalendar Vocabulary (iCalendar) + iCalendar is an Internet standard for exchanging calendar and scheduling data across different applications and platforms using a standardized text-based format (.ics). It enables interoperability for events, tasks, and scheduling, supporting features like recurring events, invitations, and time zone adjustments. While widely used in applications like Google Calendar and Outlook, its complexity and partial implementations pose challenges, leading to efforts to integrate it with Semantic Web technologies for enhanced data linking and automation. + Dan Connolly, W3C, Libby Miller, ASemantics + RDF + 2004/04/07 + Open Publication License + https://www.w3.org/2002/12/cal/ + Events + Calendar and Scheduling + 1.14 + + + OEO + The Open Energy Ontology (OEO) + The Open Energy Ontology (OEO) is a domain ontology of the energy system analysis context. It is developed as part of the Open Energy Family. The OEO is published on GitHub under an open source license. The language used is the Manchester OWL Syntax, which was chosen because it is user-friendly for editing and viewing differences of edited files. The OEO is constantly being extended. The first version of the OEO has been released on June 11th 2020. A Steering Committee (OEO-SC) was created to accompany the development, increase awareness of the ontology and include it in current projects. OWL + 03/2025 + Creative Commons Attribution 1.0 Generic (CC BY 1.0) + https://github.com/OpenEnergyPlatform/ontology?tab=readme-ov-file + Ecology and Environment + Energy + 2.7.0 + + + SPDocument + SMART Protocols Ontology: Document Module (SP-Document) + SMART Protocols Ontology: Document Module is an ontology designed to represent metadata used to report an experimental protocol. + http://oxgiraldo.wordpress.com + OWL + 2013-07-01 Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/OCDO/cmso/tree/main - Materials Science and Engineering + https://github.com/SMARTProtocols/SMART-Protocols + Scholarly Knowledge Materials Science - 0.0.1 + 4.0 - - PKO - Provenance Knowledge Ontology (PKO) - Procedural Knowledge (PK) is knowing how to perform some tasks, as opposed to descriptive/declarative knowledge, which is knowing what in terms of facts and notions. In industry, PK refers in general to structured processes to be followed, and can be related to both production (e.g., procedure on the production line in a plant) and services (e.g., procedure for troubleshooting during customer support); to specific technical expertise (e.g., procedure to set up a specific machine) and general regulations and best practices (e.g., safety procedures, activities to minimise environmental impact). - Mario Scrocca (Cefriel), Valentina Carriero (Cefriel) + + ENM + Environmental Noise Measurement Ontology (ENM) + The eNanoMapper project (https://www.enanomapper.net/), NanoCommons project (https://www.nanocommons.eu/) and ACEnano project (http://acenano-project.eu/) are creating a pan-European computational infrastructure for toxicological data management for ENMs, based on semantic web standards and ontologies. This ontology is an application ontology targeting the full domain of nanomaterial safety assessment. It re-uses several other ontologies including the NPO, CHEMINF, ChEBI, and ENVO. + eNanoMapper Consortium + OWL + 2025-02-17 + Creative Commons 3.0 + https://terminology.tib.eu/ts/ontologies/ENM + Medicine + Material Science and Engineering + 10.0 + + + DEB + Devices, Experimental scaffolds and Biomaterials Ontology (DEB) + The devices, experimental scaffolds, and biomaterials ontology (DEB) is an open resource for organizing information about biomaterials, their design, manufacture, and biological testing. It was developed using text analysis for identifying ontology terms from a biomaterials gold standard corpus, systematically curated to represent the domain's lexicon. Topics covered were validated by members of the biomaterials research community. + Osnat Hakimi + OWL + Jun 2, 2021 + GPL-3.0 + https://github.com/ProjectDebbie/Ontology_DEB + Medicine + Biomaterials + 06/2021 + + + FRBRoo + Functional Requirements for Bibliographic Records - object-oriented (FRBRoo) + The FRBRoo (Functional Requirements for Bibliographic Records - object-oriented) initiative is a joint effort of the CIDOC Conceptual Reference Model and Functional Requirements for Bibliographic Records international working groups to establish a formal ontology intended to capture and represent the underlying semantics of bibliographic information and to facilitate the integration, mediation, and interchange of bibliographic and museum information. RDF - 2025-03-01 + November 2015 Creative Commons 4.0 - https://github.com/perks-project/pk-ontology/tree/master - Industry - Provenance + https://ontome.net/namespace/6#summary + Scholarly Knowledge + Bibliographic Records + 2.4 + + + VOAF + Vocabulary of a Friend (VOAF) + The Vocabulary of a Friend (VOAF) is a vocabulary specification providing elements allowing the description of vocabularies (RDFS vocabularies or OWL ontologies). It is based on Dublin Core and VOID. + Bernard Vatant + RDF + 2013-05-24 + Creative Commons 3.0 + https://lov.linkeddata.es/vocommons/voaf/v2.3/ + Scholarly Knowledge + Social Network + 2.3 + + + CHAMEO + Characterisation Methodology Domain Ontology (CHAMEO) + An ontology for materials characterization which represents the evolution of the CHADA template in an ontological form, allowing to generate FAIR documentation of Characterisation Experiments and that has been used as a basis for the development of a number of technique-specific or application-specific ontologies in the materials characterisation domain. CHAMEO has been used as a foundation for the definition of the new CHADA template during the CWA. + https://orcid.org/0000-0002-4181-2852, https://orcid.org/0000-0002-5174-8508, https://orcid.org/0000-0002-9668-6961 + TTL + 2024-04-12 + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/emmo-repo/domain-characterisation-methodology + Materials Science and Engineering + Materials Science 1.0.0 @@ -1035,39 +1112,31 @@ Materials 0.1 - - SPWorkflow - SMART Protocols Ontology: Workflow Module (SP-Workflow) - SP-Workflow module represents: i) the executable elements of a protocol; ii) the experimental actions and material entities that participates in instructions (sample/specimen, organisms, reagents, instruments); and iii) the order of execution of the instructions. - http://oxgiraldo.wordpress.com - OWL - 2013-07-01 - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/SMARTProtocols/SMART-Protocols - Scholarly Knowledge - Workflows - 4.0 - - - ChMO - Chemical Methods Ontology (ChMO) - The Chemical Methods Ontology contains more than 3000 classes and describes methods used to: - collect data in chemical experiments, such as mass spectrometry and electron microscopy. - prepare and separate material for further analysis, such as sample ionisation, chromatography, and electrophoresis - synthesise materials, such as epitaxy and continuous vapour deposition It also describes the instruments used in these experiments, such as mass spectrometers and chromatography columns and their outputs. + + LPBFO + Laser Powder Bed Fusion Ontology (LPBFO) + The LPBF Ontology can be used to describe the additive manufacturing of a component via Laser Powder Bed Fusion (LPBF) / Selective Laser Melting (SLM). The ontology builds on BFO2.0 and BWMD_mid and has been developed to be used in conjunction with the digital workflows provided by Fraunhofer IWM. If possible, the terminology within this ontology was used as provided by ISO/ASTM 52900:2015. Recently, classes relevant for Life Cycle Analysis (LCA) were added that enable sustainability assessment. + Fraunhofer IWM OWL - 2022-04-19 + 2022-09-20 Creative Commons 4.0 - https://github.com/rsc-ontologies/rsc-cmo - Chemistry + https://matportal.org/ontologies/LPBFO + Materials Science and Engineering + Materials Science + 1.1.9 - - UMBEL - Upper Mapping and Binding Exchange Layer Vocabulary (UMBEL) - UMBEL is the Upper Mapping and Binding Exchange Layer, designed to help content interoperate on the Web. UMBEL provides two valuable functions: First, it is a broad, general reference structure of 34,000 concepts, which provides a scaffolding to link and interoperate other datasets and domain vocabularies. Second, it is a base vocabulary for the construction of other concept-based domain ontologies, also designed for interoperation. - n3 - May 10, 2016 - https://github.com/structureddynamics/UMBEL/tree/master/Ontology - General Knowledge - Web Development - 1.50 + + MusicOntology + Music Ontology (MusicOntology) + The Music Ontology Specification provides main concepts and properties fo describing music (i.e. artists, albums and tracks) on the Semantic Web. + Knowledge Media Institute, Open University + RDF + 2013/07/22 + Creative Commons 4.0 + https://github.com/motools/musicontology + Arts and Humanities + Music Theory + 2.1.5 PROCO @@ -1082,296 +1151,153 @@ Chemicals, Processes 04-14-2022 - - AUTO - Automotive Ontology (AUTO) - The AUTOMOTIVE ONTOLOGY (AUTO) defines the shared conceptual structures in the automotive industry. It is an OWL ontology. It is built upon the auto schema.org extension created by the W3C Automotive Ontology Community Group. AUTO's development process follows the best practices established by the EDMC FIBO Community. - EDM Council - RDF - 2021-03-01 - MIT - https://github.com/edmcouncil/auto/tree/master - Industry - Automotive - - - LODE - Linking Open Descriptions of Events (LODE) - People conventionally refer to an action or occurrence taking place at a certain time at a specific location as an event. This notion is potentially useful for connecting individual facts recorded in the rapidly growing collection of linked data sets and for discovering more complex relationships between data. The LODE provide an overview and comparison of existing event models, looking at the different choices they make of how to represent events. It is a model for publishing records of events as Linked Data. A tools for populating this model and a prototype “event directory” web service, which can be used to locate stable URIs for events that have occurred, provide RDFS+OWL descriptions and link to related resources. - Ryan Shaw - RDF - 2020-10-31 - Creative Commons Attribution 3.0 - https://linkedevents.org/ontology/ - Events - 2020-10-31 + + Atomistic + Atomistic Ontology (Atomistic) + An EMMO-based domain ontology for atomistic and electronic modelling. + Francesca L. Bleken, Jesper Friis + TTL + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/emmo-repo/domain-atomistic + Materials Science and Engineering + Materials Science + 0.0.2 - - SIO - Semanticscience Integrated Ontology (SIO) - The semanticscience integrated ontology (SIO) provides a simple, integrated upper level ontology (types, relations) for consistent knowledge representation across physical, processual and informational entities. This project provides foundational support for the Bio2RDF (http://bio2rdf.org) and SADI (http://sadiframework.org) projects. - M. Dumontier + + VIBSO + Vibrational Spectroscopy Ontology (VIBSO) + The Vibration Spectroscopy Ontology defines technical terms with which research data produced in vibrational spectroscopy experiments can be semantically enriched, made machine readable and FAIR. + VIBSO Workgroup OWL - 03/25/2024 - Creative Commons 4.0 - https://bioportal.bioontology.org/ontologies/SIO - Upper Ontology - Basic - 1.59 + 2024-09-23 + Creative Commons Attribution 4.0 + https://terminology.tib.eu/ts/ontologies/vibso + Chemistry + Spectroscopy + 2024-09-23 - - MicroStructures - EMMO-based ontology for microstructures (MicroStructures) - This is intended to be a domain ontology for metallic microstructures, covering aspects like: composition, particles, both stable (primary) and metastable (precipitates), grains, subgrains, grain boundaries & particle free zones (PFZs), texture, dislocations. The aim is to support both microstructure modelling as well as characterisation. + + OBOE + Extensible Observation Ontology (OBOE) + The Extensible Observation Ontology (OBOE) is a formal ontology for capturing the semantics of scientific observation and measurement. The ontology supports researchers to add detailed semantic annotations to scientific data, thereby clarifying the inherent meaning of scientific observations. + The Regents of the University of California OWL - https://github.com/jesper-friis/emmo-microstructure - Materials Science and Engineering - Microstructure - - - DCAT - Data Catalog Vocabulary (DCAT) - Data Catalog Vocabulary (DCAT) is an RDF vocabulary designed to facilitate interoperability between data catalogs published on the Web. This document defines the schema and provides examples for its use. DCAT enables a publisher to describe datasets and data services in a catalog using a standard model and vocabulary that facilitates the consumption and aggregation of metadata from multiple catalogs. This can increase the discoverability of datasets and data services. It also makes it possible to have a decentralized approach to publishing data catalogs and makes federated search for datasets across catalogs in multiple sites possible using the same query mechanism and structure. Aggregated DCAT metadata can serve as a manifest file as part of the digital preservation process. - Digital Enterprise Research Institute (DERI) - RDF - 22 August 2024 - W3C Document License - https://www.w3.org/TR/vocab-dcat-3/ + Creative Commons 3.0 + https://terminology.tib.eu/ts/ontologies/OBOE Scholarly Knowledge - Data Catalogs - 3.0 + Scientific Observation + 1.2 - - MOLBRINELL - MatoLab Brinell Test Ontology (MOL_BRINELL) - An ontology for describing the Brinell hardness testing process, made in the Materials Open Lab Project. - Birgit Skrotzki, Hossein Beygi Nasrabadi, Philipp von Hartrott, Vinicius Carrillo Beber, Yue Chen + + OWLTime + Time Ontology in OWL (OWL-Time) + OWL-Time is an OWL-2 DL ontology of temporal concepts, for describing the temporal properties of resources in the world or described in Web pages. The ontology provides a vocabulary for expressing facts about topological (ordering) relations among instants and intervals, together with information about durations, and about temporal position including date-time information. Time positions and durations may be expressed using either the conventional (Gregorian) calendar and clock, or using another temporal reference system such as Unix-time, geologic time, or different calendars. + World Wide Web Consortium TTL - 05/05/2022 - https://matportal.org/ontologies/MOL_BRINELL - Materials Science and Engineering - Materials Testing - 0.1 + 15 November 2022 + W3C Software Notice and Document License + https://www.w3.org/TR/owl-time/ + Units and Measurements + Temporal Reasoning + 1.0 - - AMOntology - Additive Manufacturing Ontology (AMOntology) - The AM ontology has been developed following two major milestones. The ontology developed within the first milestone includes AMProcessOntology, ModelOntology and AMOntology files. AMProcessOntology contains the set of entities used to capture knowledge about additive manufacturing processes. ModelOntology contains the set of entities used to capture knowledge about modeling concepts that represent (possibly) multi-physics multi-scale processes. AMOntology uses AMProcessOntology and ModelOntology files to describe entities that capture knowledge about characteristics of computational models for AM processes. - Iassou Souroko, Ali Riza Durmaz + + BMO + Building Material Ontology (BMO) + Building Material Ontology defines the main concepts of building material, types, layers, and properties. + Janakiram Karlapudi, Prathap Valluru TTL - 2023-05-10 + 2019-12-10 Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/iassouroko/AMontology + https://matportal.org/ontologies/BUILDMAT Materials Science and Engineering - Manufacturing - 1.0 - - - AFO - Allotrope Foundation Ontology (AFO) - The AFO is an ontology suite that provides a standard vocabulary and semantic model for the representation of laboratory analytical processes. The AFO suite is aligned at the upper layer to the Basic Formal Ontology (BFO). The core domains modeled include, Equipment, Material, Process, and Results. This artifact contains all triples of Allotrope Foundation Merged Without QUDT Ontology Suite (REC/2023/12) together with triples inferred with HermiT. - Allotrope Foundation - TTL - 2024-06-28 - CC BY 4.0 - https://terminology.tib.eu/ts/ontologies/AFO - Chemistry - Laboratory Analytical Processes - 2024-06 - - - DOAP - The Description of a Project vocabulary (DOAP) - The Description of a Project vocabulary (DOAP), described using W3C RDF Schema and the Web Ontology Language to describe software projects, and in particular open source projects. - Edd Wilder-James - RDF - 2020-04-03 - Apache License 2.0 - https://github.com/ewilderj/doap/blob/master/schema/doap.rdf - Industry - Software - - - AgrO - Agronomy Ontology (AgrO) - An ontology is a formal representation of a disciplinary domain, representing a semantic standard that can be employed to annotate data where key concepts are defined, as well as the relationships that exist between those concepts (Gruber, 2009). Ontologies provide a common language for different kinds of data to be easily interpretable and interoperable allowing easier aggregation and analysis. The Agronomy Ontology (AgrO) provides terms from the agronomy domain that are semantically organized and can facilitate the collection, storage and use of agronomic data, enabling easy interpretation and reuse of the data by humans and machines alike. To fully understand the implications of varying practices within cropping systems and derive insights, it is often necessary to pull together information from data in different disciplinary domains. For example, data on field management, soil, weather and crop phenotypes may need to be aggregated to assess performance of particular crop under different management interventions. However, agronomic data are often collected, described, and stored in inconsistent ways, impeding data comparison, mining, interpretation reuse. The use of standards for metadata and data annotation play a key role in addressing these challenges. While the CG Core Metadata Schema provides a metadata standard to describe agricultural datasets, the Agronomy Ontology enables the description of agronomic data variables using standard terms. - The Crop Ontology Consortium - RDF - 2022-11-02 - Creative Commons 4.0 - https://agroportal.lirmm.fr/ontologies/AGRO?p=summary - Agriculture - Agronomy - 1.0 - - - Juso - Juso Ontology (Juso) - Juso Ontology is a Web vocabulary for describing geographical addresses and features. - James G. Kim, LiST Inc. - TTL - 2015-11-10 - Creative Commons 4.0 - https://rdfs.co/juso/0.1.1/html - Geography - geographical knowledge - 0.1.1 - - - CCO - Common Core Ontologies (CCO) - The Common Core Ontologies (CCO) is a widely-used suite of eleven ontologies that consist of logically well-defined generic terms and relations among them reflecting entities across all domains of interest. - TTL - 2024-11-06 - BSD-3-Clause license - https://github.com/CommonCoreOntology/CommonCoreOntologies - General Knowledge - General - 2.0 - - - PATO - Phenotype and Trait Ontology (PATO) - An ontology of phenotypic qualities (properties, attributes or characteristics). - OWL - 2025-02-01 - Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/PATO - Biology and Life Sciences - Biology - 1.2 + Materials + 0.1 - - EMMO - The Elementary Multiperspective Material Ontology (EMMO) - The Elementary Multiperspective Material Ontology (EMMO) is the result of a multidisciplinary effort within the EMMC, aimed at the development of a standard representational ontology framework based on current materials modelling and characterization knowledge. Instead of starting from general upper level concepts, as done by other ontologies, the EMMO development started from the very bottom level, using the actual picture of the physical world coming from applied sciences, and in particular from physics and material sciences. - European Materials Modelling Council (EMMC) + + SystemCapabilities + System Capabilities Ontology (SystemCapabilities) + This ontology describes system capabilities, operating ranges, and survival ranges. + W3C/OGC Spatial Data on the Web Working Group OWL - 2024-03 - Creative Commons 4.0 - https://emmo-repo.github.io/ + 2017-05-14 + W3C Software and Document License + https://terminology.tib.eu/ts/ontologies/SSNSYSTEM Materials Science and Engineering - Materials Modelling - 1.0.0-rc3 - - - MFOEM - Mental Functioning Ontology of Emotions - Emotion Module (MFOEM) - The Mental Functioning Ontology - Emotion Module (MFOEM) aims to include all relevant aspects of affective phenomena including their bearers, the different types of emotions, moods, etc., their different parts and dimensions of variation, their facial and vocal expressions, and the role of emotions and affective phenomena in general in influencing human behavior.This class processes Mental Functioning Ontology of Emotions (MFOEM) using default behavior. - Swiss Centre for Affective Sciences & University at Buffalo - OWL - Creative Commons 3.0 - http://purl.obolibrary.org/obo/MFOEM.owl - Medicine - Emotion - - - DUO - Data Use Ontology (DUO) - DUO is an ontology which represent data use conditions. - OWL - 2025-02-17 - Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/DUO/ - Scholarly Knowledge - 1.0 - - - ATOL - Animal Trait Ontology for Livestock (ATOL) - ATOL (Animal Trait Ontology for Livestock) is an ontology of characteristics defining phenotypes of livestock in their environment (EOL). ATOL aims to: - provide a reference ontology of phenotypic traits of farm animals for the international scientific and educational - communities, farmers, etc.; - deliver this reference ontology in a language which can be used by computers in order to support database management, semantic analysis and modeling; - represent traits as generic as possible for livestock vertebrates; - make the ATOL ontology as operational as possible and closely related to measurement techniques; - structure the ontology in relation to animal production. - INRAE, France - OWL - May 11, 2020 - Creative Commons 4.0 - https://bioportal.bioontology.org/ontologies/ATOL - Agriculture - Animal Science - 6.0 - - - BBCBusiness - BBC Business News Ontology (BBCBusiness) - The Business News Ontology describes the concepts that occur in BBC business news. - https://www.bbc.co.uk/blogs/internet/authors/Jeremy_Tarling, https://uk.linkedin.com/in/amaalmohamed - TTL - 2014-11-09 - Creative Commons 4.0 - https://www.bbc.co.uk/ontologies/business-news-ontology - News and Media - Business News - 0.5 + Materials Science, Engineering, Systems - - MAT - Material Properties Ontology (MAT) - The Material Properties Ontology aims to provide the vocabulary to describe the building components, materials, and their corresponding properties, relevant within the construction industry. More specifically, the building elements and properties covered in this ontology support applications focused on the design of building renovation projects. - María Poveda-Villalón, Serge Chávez-Feria - RDF - Creative Commons 4.0 - https://bimerr.iot.linkeddata.es/def/material-properties/ - Materials Science and Engineering - Materials Properties - 0.0.8 + + Conference + Conference Ontology (Conference) + The conference-ontology is a new self-contained ontology for modeling knowledge about conferences. The conference-ontology adopts the best ontology design practices (e.g., Ontology Design Patterns, ontology reuse and interlinking) and guarantees interoperability with SWC ontology and all other pertinent vocabularies. + Aldo Gangemi et al. + OWL + 2016/04/30 + Creative Commons 3.0 + http://www.scholarlydata.org/ontology/conference-ontology.owl + Events + Conferences - - SUMO - Suggested Upper Merged Ontology (SUMO) - The Suggested Upper Merged Ontology (SUMO) and its domain ontologies form the largest formal public ontology in existence today. They are being used for research and applications in search, linguistics and reasoning. SUMO is the only formal ontology that has been mapped to all of the WordNet lexicon. + + EDAM + The ontology of data analysis and management (EDAM) + EDAM is a domain ontology of data analysis and data management in bio- and other sciences, and science-based applications. It comprises concepts related to analysis, modelling, optimisation, and data life cycle. Targetting usability by diverse users, the structure of EDAM is relatively simple, divided into 4 main sections: Topic, Operation, Data (incl. Identifier), and Format. + Federico Bianchini, Hervé Ménager, Jon Ison, Matúš Kalaš OWL - 2025-02-17 + 24.09.2024 Creative Commons 4.0 - https://www.ontologyportal.org/ - Upper Ontology - 1.0 + https://terminology.tib.eu/ts/ontologies/edam + General Knowledge + General + 1.25-20240924T0027Z-unstable(1.26) - - DublinCore - Dublin Core Vocabulary (DublinCore) - The Dublin Core Schema is a small set of vocabulary terms that can be used to describe several kinds of resources. Dublin Core Metadata may be used for multiple purposes, from simple resource description, to combining metadata vocabularies of different metadata standards, to providing interoperability for metadata vocabularies in the Linked Data cloud and Semantic Web implementations. - The Dublin Core Metadata Initiative + + LODE + Linking Open Descriptions of Events (LODE) + People conventionally refer to an action or occurrence taking place at a certain time at a specific location as an event. This notion is potentially useful for connecting individual facts recorded in the rapidly growing collection of linked data sets and for discovering more complex relationships between data. The LODE provide an overview and comparison of existing event models, looking at the different choices they make of how to represent events. It is a model for publishing records of events as Linked Data. A tools for populating this model and a prototype “event directory” web service, which can be used to locate stable URIs for events that have occurred, provide RDFS+OWL descriptions and link to related resources. + Ryan Shaw RDF - February 17, 2017 - Public Domain - https://bioportal.bioontology.org/ontologies/DC - General Knowledge - Metadata - 1.1 + 2020-10-31 + Creative Commons Attribution 3.0 + https://linkedevents.org/ontology/ + Events + 2020-10-31 - - SWEET - Semantic Web for Earth and Environment Technology Ontology (SWEET) - The Semantic Web for Earth and Environment Technology Ontology (SWEET) is an investigation in improving discovery and use of Earth science data, through software understanding of the semantics of web resources. SWEET is a collection of ontologies conceptualizing a knowledge space for Earth system science, represented using the web ontology language (OWL). It includes both orthogonal concepts (space, time, Earth realms, physical quantities, etc.) and integrative science knowledge concepts (phenomena, events, etc.). - NASA, JPL, Caltech + + PreMOn + Pre-Modern Ontology (PreMOn) + The PreMOn Ontology is an extension of lemon (W3C Ontology Lexicon Community Group, 2015) for representing predicate models and their mappings. The Core Module of the PreMOn Ontology defines the main abstractions for modelling semantic classes with their semantic roles, mappings between different predicate models, and annotations. + Francesco Corcoglioniti, Marco Rospocher <https://dkm.fbk.eu/rospocher> OWL - July 14, 2022 + 2018-02-15 Creative Commons 4.0 - https://bioportal.bioontology.org/ontologies/SWEET - Ecology and Environment - Earth Science, Geoscience - 3.6.0 + https://premon.fbk.eu/ontology/core# + Scholarly Knowledge + Linguistics + 2018a - - BBCProgrammes - BBC Programmes Ontology (BBCProgrammes) - This ontology aims at providing a simple vocabulary for describing programmes. It covers brands, series (seasons), episodes, broadcast events, broadcast services,etc. Its development was funded by the BBC, and is heavily grounded on previous programmes data modelling work done there. - https://moustaki.org/foaf.rdf#moustaki - TTL - 2009/02/20 - Creative Commons 4.0 - https://www.bbc.co.uk/ontologies/programmes-ontology - News and Media - Programmes - 1.1 + + CHEMINF + Chemical Information Ontology (CHEMINF) + The chemical information ontology (cheminf) describes information entities about chemical entities. It provides qualitative and quantitative attributes to richly describe chemicals. Includes terms for the descriptors commonly used in cheminformatics software applications and the algorithms which generate them. + Egon Willighagen, Nina Jeliazkova, Ola Spjuth, Valery Tkachenko + OWL + Creative Commons 1.0 + https://terminology.tib.eu/ts/ontologies/CHEMINF + Chemistry + 2.1.0 - - BattINFO - Battery Interface Ontology (BattINFO) - BattINFO is a foundational resource for harmonizing battery knowledge representation and enhancing data interoperability. The primary objective is to provide the necessary tools to create FAIR (Findable, Accessible, Interoperable, Reusable) battery data that can be integrated into the Semantic Web. - TTL - https://github.com/BIG-MAP/BattINFO + + CMSO + Computational Material Sample Ontology (CMSO) + CMSO is an ontology that aims to describe computational materials science samples (or structures), including crystalline defects. Initially focusing on the description at the atomic scale. + https://orcid.org/0000-0001-7564-7990 + OWL + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/OCDO/cmso/tree/main Materials Science and Engineering Materials Science + 0.0.1 NPO @@ -1386,156 +1312,23 @@ Materials Science 2013-05-31 - - BBCFood - BBC Food Ontology (BBCFood) - The Food Ontology is a simple lightweight ontology for publishing data about recipes, including the foods they are made from and the foods they create as well as the diets, menus, seasons, courses and occasions they may be suitable for. Whilst it originates in a specific BBC use case, the Food Ontology should be applicable to a wide range of recipe data publishing across the web. - TTL - 2014/03/18 - Creative Commons 4.0 - https://www.bbc.co.uk/ontologies/food-ontology - News and Media - Food and Beverage - 0.1 - - - MGED - MGED Ontology (MGED) - An ontology for microarray experiments in support of MAGE v.1. Concepts, definitions, terms, and resources for standardized description of a microarray experiment in support of MAGE v.1. The MGED ontology is divided into the MGED Core ontology which is intended to be stable and in synch with MAGE v.1; and the MGED Extended ontology which adds further associations and classes not found in MAGE v.1 - Chris Stoeckert, Helen Parkinson, Trish Whetzel, Paul Spellman, Catherine A. Ball, Joseph White, John Matese, Liju Fan, Gilberto Fragoso, Mervi Heiskanen, Susanna Sansone, Helen Causton, Laurence Game, Chris Taylor - OWL - Feb. 9, 2007 - Creative Commons 4.0 - https://mged.sourceforge.net/ontologies/MGEDontology.php/ - Biology and Life Sciences - Domain Ontology - 1.3.1.1 - - - DSIM - Dislocation Simulation and Model Ontology (DSIM) - Dislocation simulation and model ontology (DSIM) is an ontology developed to model various concepts and relationships in the discrete dislocation dynamics domain and microscopy techniques used in the dislocation domain. The various concepts are the numerical representation of dislocation applied in the dislocation dynamic simulation and the pictorial concept of pixel applied in representing dislocation in the experimental image, eg., TEM image, SEM image, and FIM image. - Ahmad Zainul Ihsan - OWL - 17.08.2023 - Creative Commons Attribution 3.0 Unported (CC BY 3.0) - https://github.com/OCDO/DSIM - Materials Science and Engineering - Materials Science - 1.0 - - - Common - Common Ontology (Common) - Ontology for the representation of commons elements in the Trias ontology - Jhon Toledo, Miguel Angel García, Oscar Corcho - RDF - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://w3id.org/mobility/trias/common/0.1.0 - Education - Computer Science - 0.1.0 - - - BBCStoryline - BBC Storyline Ontology (BBCStoryline) - The News Storyline Ontology is a generic model for describing and organising the stories news organisations tell. The ontology is intended to be flexible to support any given news or media publisher's approach to handling news stories. At the heart of the ontology, is the concept of Storyline. As a nuance of the English language the word 'story' has multiple meanings. In news organisations, a story can be an individual piece of content, such as an article or news report. It can also be the editorial view on events occurring in the world. - http://uk.linkedin.com/in/paulwilton, http://www.bbc.co.uk/blogs/internet/authors/Jeremy_Tarling, http://uk.linkedin.com/in/jarredmcginnis - TTL - 2013-05-01 - Creative Commons 4.0 - https://iptc.org/thirdparty/bbc-ontologies/storyline.html - News and Media - Storyline - 0.3 - - - MMO - Materials Mechanics Ontology (MMO) - The materials mechanics ontology is an application-level ontology that was created for supporting named entity recognition tasks for materials fatigue domain. The ontology covers some fairly general MSE concepts that could prospectively be merged into PMDco or other upper materials ontologies such as descriptions of crystallographic defects and microstructural entities. Furthermore, concepts related to the materials fatigue subdomain are also heavily incorporated. - Akhil Thomas, Ali Riza Durmaz - RDF - 2024-01-30 - Creative Commons 4.0 - https://iwm-micro-mechanics-public.pages.fraunhofer.de/ontologies/materials-mechanics-ontology/index-en.html - Materials Science and Engineering - Scholarly Knowledge - 1.0.1 - - - MAMBO - Molecules And Materials Basic Ontology (MAMBO) - MAMBO (Molecules And Materials Basic Ontology) is a domain ontology for molecular materials. Its main targets are: Allowing the retrieval of structured information regarding molecular materials and related applications (i.e. devices based on molecular materials) Supporting the development of new, complex workflows for modelling systems based on molecular materials (computational modelling and data-driven techniques) Integrating data generated via computational simulations and empirical experiments. - OWL - General Public License v3.0 (GPL-3.0) - https://github.com/daimoners/MAMBO - Materials Science and Engineering - Materials Science - - - EXPO - Ontology of Scientific Experiments (EXPO) - Formalise generic knowledge about scientific experimental design, methodology, and results representation. - OWL - Academic Free License (AFL) - https://expo.sourceforge.net/ - Scholarly Knowledge - Scientific Experiments - - - FOAF - Friend of a Friend (FOAF) - FOAF is a project devoted to linking people and information using the Web. Regardless of whether information is in people's heads, in physical or digital documents, or in the form of factual data, it can be linked. - Dan Brickley, Libby Miller - RDF - 14 January 2014 - Creative Commons - http://xmlns.com/foaf/0.1/ - Social Sciences - Social - 0.1 - - - QUDV - Quantities, Units, Dimensions and Values (QUDV) - The SysML QUDV (Quantities, Units, Dimensions and Values) modelLibrary is specified in a UML/SysML class/block diagram. In order to generalize its potential usage and alignment with other standardization efforts concerning quantities and units, it is of interest to verify that the QUDV model can also be represented in the form of an ontology using a formal ontology definition language. - SysML - OWL - 2009-10-30 - Apache License 2.0 - https://www.omgwiki.org/OMGSysML/doku.php?id=sysml-qudv:qudv_owl - Units and Measurements - 2009-10-30 - - - DEB - Devices, Experimental scaffolds and Biomaterials Ontology (DEB) - The devices, experimental scaffolds, and biomaterials ontology (DEB) is an open resource for organizing information about biomaterials, their design, manufacture, and biological testing. It was developed using text analysis for identifying ontology terms from a biomaterials gold standard corpus, systematically curated to represent the domain's lexicon. Topics covered were validated by members of the biomaterials research community. - Osnat Hakimi - OWL - Jun 2, 2021 - GPL-3.0 - https://github.com/ProjectDebbie/Ontology_DEB - Medicine - Biomaterials - 06/2021 - - - HPOnt - The Heat Pump Ontology (HPOnt) - The Heat Pump Ontology (HPOnt) aims to formalize and represent all the relevant information of Heat Pumps. The HPOnt has been developed as part of the REACT project which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 824395. - REACT project team - OWL - Creative Commons 4.0 - https://react2020.github.io/REACT-ONTOLOGY/HPOnt/index-en.html/ - Materials Science and Engineering - Materials Science - 0.2 + + PKO + Provenance Knowledge Ontology (PKO) + Procedural Knowledge (PK) is knowing how to perform some tasks, as opposed to descriptive/declarative knowledge, which is knowing what in terms of facts and notions. In industry, PK refers in general to structured processes to be followed, and can be related to both production (e.g., procedure on the production line in a plant) and services (e.g., procedure for troubleshooting during customer support); to specific technical expertise (e.g., procedure to set up a specific machine) and general regulations and best practices (e.g., safety procedures, activities to minimise environmental impact). + Mario Scrocca (Cefriel), Valentina Carriero (Cefriel) + RDF + 2025-03-01 + Creative Commons 4.0 + https://github.com/perks-project/pk-ontology/tree/master + Industry + Provenance + 1.0.0 - - OIEManufacturing - Open Innovation Environment Manufacturing (OIEManufacturing) - The manufacturing module populates the physicalistic perspective with manufacturing subclasses categorised according to modern applied physical sciences. + + OIEModels + Open Innovation Environment Models (OIEModels) + The models module defines models as semiotic signs that stands for an object by resembling or imitating it, in shape or by sharing a similar logical structure. Adham Hashibon, Daniele Toti, Emanuele Ghedini, Georg J. Schmitz, Gerhard Goldbeck, Jesper Friis, Pierluigi Del Nostro TTL Creative Commons Attribution 4.0 International (CC BY 4.0) @@ -1543,244 +1336,252 @@ Materials Science and Engineering Materials - - MassSpectrometry - Mass Spectrometry Ontology (MassSpectrometry) - A structured controlled vocabulary for the annotation of experiments concerned with proteomics mass spectrometry. - Andreas Bertsch + + PO + Plant Ontology (PO) + The Plant Ontology (PO) is a structured vocabulary and database resource that links plant anatomy, morphology and growth and development to plant genomics data. OWL - 12:02:2025 Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/MS - Chemistry - Mass Spectrometry, Proteomics + https://github.com/Planteome/plant-ontology + Agriculture + Plant Anatomy, Morphology, Growth and Development - - BMO - Building Material Ontology (BMO) - Building Material Ontology defines the main concepts of building material, types, layers, and properties. - Janakiram Karlapudi, Prathap Valluru - TTL - 2019-12-10 - Creative Commons Attribution 4.0 International (CC BY 4.0) - https://matportal.org/ontologies/BUILDMAT + + MMO + Materials Mechanics Ontology (MMO) + The materials mechanics ontology is an application-level ontology that was created for supporting named entity recognition tasks for materials fatigue domain. The ontology covers some fairly general MSE concepts that could prospectively be merged into PMDco or other upper materials ontologies such as descriptions of crystallographic defects and microstructural entities. Furthermore, concepts related to the materials fatigue subdomain are also heavily incorporated. + Akhil Thomas, Ali Riza Durmaz + RDF + 2024-01-30 + Creative Commons 4.0 + https://iwm-micro-mechanics-public.pages.fraunhofer.de/ontologies/materials-mechanics-ontology/index-en.html Materials Science and Engineering - Materials - 0.1 - - - PSIMOD - Protein Modifications Ontology (PSIMOD) - PSI-MOD is an ontology developed by the Proteomics Standards Initiative (PSI) that describes protein chemical modifications, logically linked by an is_a relationship in such a way as to form a direct acyclic graph (DAG). The PSI-MOD ontology has more than 45 top-level nodes, and provides alternative hierarchical paths for classifying protein modifications either by the molecular structure of the modification, or by the amino acid residue that is modified. - OWL - 2022-06-13 - Creative Commons Attribution 4.0 - https://github.com/HUPO-PSI/psi-mod-CV - Chemistry - Protein Modifications - 1.031.6 + Scholarly Knowledge + 1.0.1 - - SSN - Semantic Sensor Network Ontology (SSN) - The Semantic Sensor Network (SSN) ontology is an ontology for describing sensors and their observations, the involved procedures, the studied features of interest, the samples used to do so, and the observed properties, as well as actuators. SSN follows a horizontal and vertical modularization architecture by including a lightweight but self-contained core ontology called SOSA (Sensor, Observation, Sample, and Actuator) for its elementary classes and properties. With their different scope and different degrees of axiomatization, SSN and SOSA are able to support a wide range of applications and use cases, including satellite imagery, large-scale scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Web of Things. Both ontologies are described below, and examples of their usage are given. - W3C/OGC Spatial Data on the Web Working Group + + Nomisma + Nomisma Ontology (Nomisma) + Nomisma Ontology is a collaborative project to provide stable digital representations of numismatic concepts according to the principles of Linked Open Data. These take the form of http URIs that provide access to the information about a concept in various formats. The project is a collaborative effort of the American Numismatic Society and the Institute for the Study of the Ancient World at New York University. + American Numismatic Society, Institute for the Study of the Ancient World TTL - 2017-04-17 - http://www.w3.org/Consortium/Legal/2015/copyright-software-and-document - https://github.com/w3c/sdw-sosa-ssn/tree/482484fe2edc1ba8aa7f19214a72bdb77123e833 - Materials Science and Engineering - Sensor Networks - 1.0 + 2025-01-22 + Creative Commons 4.0 + https://www.dainst.org/forschung/projekte/noslug/2098 + Arts and Humanities + Numismatics - - LexInfo - LexInfo (LexInfo) - LexInfo allows us to associate linguistic information to elements in an ontology with respect to any level of linguistic description and expressivity. LexInfo has been implemented as an OWL ontology and is available together with an API. + + TUBES + TUBES System Ontology (TUBES) + The scope of the TUBES System Ontology is to explicitly define interconnected building service system in the AECO industry, their hierarchical subdivisions, structural and functional aspects, and links to spatial entities. As such, TSO supports the effort to represent linkable information in a future semantic web of building data. It has a strong alignment to other ontologies within the W3C community. + Nicolas Pauen RDF - Apache 2.0 - https://lexinfo.net/index.html - Scholarly Knowledge - Linguistics - 3.0 + 2022-02-01 + Creative Commons 4.0 + https://rwth-e3d.github.io/tso/ + Industry + Building Services + 0.3.0 - - PRotein - Protein Ontology (PRO) - The PRotein Ontology (PRO) formally defines taxon-specific and taxon-neutral protein-related entities in three major areas: proteins related by evolution; proteins produced from a given gene; and protein-containing complexes. + + OM + Ontology of Units of Measure (OM) + The Ontology of units of Measure (OM) models concepts and relations important to scientific research. It has a strong focus on units, quantities, measurements, and dimensions. It includes, for instance, common units such as the SI units metre and kilogram, but also units from other systems of units such as the mile or nautical mile. For many application areas it includes more specific units and quantities, such as the unit of the Hubble constant or the quantity vaselife. The following application areas are supported by OM: Geometry; Mechanics; Thermodynamics; Electromagnetism; Fluid mechanics; Chemical physics; Photometry; Radiometry and Radiobiology; Nuclear physics; Astronomy and Astrophysics; Cosmology; Earth science; Meteorology; Material science; Microbiology; Economics; Information technology and Typography. + Hajo Rijgersberg, Don Willems, Jan Top RDF - 08:08:2024 + June 28, 2024 Creative Commons 4.0 - http://purl.obolibrary.org/obo/pr.owl - Medicine - Protein - 1.2 + https://bioportal.bioontology.org/ontologies/OM + Units and Measurements + 2.0.57 - - FIX - FIX Ontology (FIX) - An ontology of physico-chemical methods and properties. + + SPWorkflow + SMART Protocols Ontology: Workflow Module (SP-Workflow) + SP-Workflow module represents: i) the executable elements of a protocol; ii) the experimental actions and material entities that participates in instructions (sample/specimen, organisms, reagents, instruments); and iii) the order of execution of the instructions. + http://oxgiraldo.wordpress.com OWL - 2020-04-13 - https://terminology.tib.eu/ts/ontologies/FIX - Chemistry - Chemicals, Properties - 2020-04-13 + 2013-07-01 + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/SMARTProtocols/SMART-Protocols + Scholarly Knowledge + Workflows + 4.0 - - BIBFRAME - Bibliographic Framework Ontology (BIBFRAME) - The Bibframe vocabulary consists of RDF classes and properties used for the description of items cataloged principally by libraries, but may also be used to describe items cataloged by museums and archives. Classes include the three core classes - Work, Instance, and Item - in addition to many more classes to support description. Properties describe characteristics of the resource being described as well as relationships among resources. For example: one Work might be a "translation of" another Work; an Instance may be an "instance of" a particular Bibframe Work. Other properties describe attributes of Works and Instances. For example: the Bibframe property "subject" expresses an important attribute of a Work (what the Work is about), and the property "extent" (e.g. number of pages) expresses an attribute of an Instance. - United States, Library of Congress - RDF - 2022-10-03 - Creative Commons 1.0 - https://id.loc.gov/ontologies/bflc.html - Education - Library, Museums, Archives - 2.5.0 + + BBCProvenance + BBC Provenance News Ontology (BBCProvenance) + An ontology to capture data about the provenance of data in an RDF Triple Store. This provenance is focused on the immediate providers and not the ultimate source, so for example, this would record that geodata was provided by the BBC Locator team, and not geonames. In the Linked Data Platform, this data is applied to contexts or named graphs. A named graph is, in effect, a 'fourth part' to a triple, hence the term 'quad store'. + LinkedData@bbc.co.uk + TTL + 2012-12-01 + Creative Commons 4.0 + https://www.bbc.co.uk/ontologies/provenance-ontology + News and Media + Provenance + 1.9 - - OBI - Ontology for Biomedical Investigations (OBI) - The Ontology for Biomedical Investigations (OBI) helps you communicate clearly about scientific investigations by defining more than 2500 terms for assays, devices, objectives, and more. + + EMMO + The Elementary Multiperspective Material Ontology (EMMO) + The Elementary Multiperspective Material Ontology (EMMO) is the result of a multidisciplinary effort within the EMMC, aimed at the development of a standard representational ontology framework based on current materials modelling and characterization knowledge. Instead of starting from general upper level concepts, as done by other ontologies, the EMMO development started from the very bottom level, using the actual picture of the physical world coming from applied sciences, and in particular from physics and material sciences. + European Materials Modelling Council (EMMC) OWL - 2025-01-09 + 2024-03 Creative Commons 4.0 - https://github.com/obi-ontology/obi/tree/master - Medicine - Biomedical Investigations + https://emmo-repo.github.io/ + Materials Science and Engineering + Materials Modelling + 1.0.0-rc3 - - REX - Physico-chemical process ontology (REX) - REX is an ontology of physico-chemical processes, i.e. physico-chemical changes occurring in course of time. REX includes both microscopic processes (involving molecular entities or subatomic particles) and macroscopic processes. Some biochemical processes from Gene Ontology (GO Biological process) can be described as instances of REX. - University of Warsaw + + LDO + Line Defect Ontology (LDO) + LDO is an ontology designed to describe line defects in crystalline materials, such as dislocations and disclinations. + https://orcid.org/0000-0001-7564-7990 OWL - 2025-03-11 - Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/REX - Chemistry - 1.0 + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/OCDO/ldo + Materials Science and Engineering + Materials Defects + 1.0.0 - - SAREF - Smart Applications REFerence ontology (SAREF) - The Smart Applications REFerence (SAREF) suite of ontologies forms a shared model of consensus intended to enable semantic interoperability between solutions from different providers and among various activity sectors in the Internet of Things (IoT), thus contributing to the development of data spaces. SAREF is published as a set of open standards produced by ETSI Technical Committee Smart Machine-to-Machine communications (SmartM2M). - ETSI Technical Committee Smart Machine-to-Machine communications (SmartM2M) + + MAT + Material Properties Ontology (MAT) + The Material Properties Ontology aims to provide the vocabulary to describe the building components, materials, and their corresponding properties, relevant within the construction industry. More specifically, the building elements and properties covered in this ontology support applications focused on the design of building renovation projects. + María Poveda-Villalón, Serge Chávez-Feria RDF - 2020-12-31 - https://saref.etsi.org/core/v3.2.1/ - Web and Internet - interoperability - 3.2.1 + Creative Commons 4.0 + https://bimerr.iot.linkeddata.es/def/material-properties/ + Materials Science and Engineering + Materials Properties + 0.0.8 - - ENVO - Environment Ontology (ENVO) - ENVO is an expressive, community ontology which helps humans, machines, and semantic web applications understand environmental entities of all kinds, from microscopic to intergalactic scales. As a FAIR-compliant resource, it promotes interoperability through the concise, controlled description of all things environmental. - Pier Luigi Buttigieg (https://orcid.org/0000-0002-4366-3088) + + ChEBI + Chemical Entities of Biological Interest (ChEBI) + Chemical Entities of Biological Interest (ChEBI) is a dictionary of molecular entities focused on ‘small’ chemical compounds. The term ‘molecular entity’ refers to any constitutionally or isotopically distinct atom, molecule, ion, ion pair, radical, radical ion, complex, conformer, etc., identifiable as a separately distinguishable entity. The molecular entities in question are either products of nature or synthetic products used to intervene in the processes of living organisms. ChEBI incorporates an ontological classification, whereby the relationships between molecular entities or classes of entities and their parents and/or children are specified. OWL - 2024-07-01 - Creative Commons 1.0 - https://obofoundry.org/ontology/envo.html - Ecology and Environment - Environment, Ecosystems, Habitats - 2024-07-01 + 01/01/2025 + Creative Commons 4.0 + https://www.ebi.ac.uk/chebi/ + Chemistry + Chemical Entities + 239 - - CIFCore - Crystallographic Information Framework Core Dictionary (CIFCore) - (1) to explain the historical development of CIF dictionaries to define in a machine-actionable manner the contents of data files covering various aspects of crystallography and related structural sciences; (2) to demonstrate some of the more complex types of information that can be handled with this approach. - TTL - May 24, 2023 + + PMDco + The Platform MaterialDigital core ontology (PMDco) + The PMD Core Ontology (PMDco) is a comprehensive framework for representing knowledge that encompasses fundamental concepts from the domains of materials science and engineering (MSE). The PMDco has been designed as a mid-level ontology to establish a connection between specific MSE application ontologies and the domain neutral concepts found in established top-level ontologies. The primary goal of the PMDco is to promote interoperability between diverse domains. + Jannis Grundmann + OWL + 2025-03-20 Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/emmo-repo/CIF-ontology?tab=readme-ov-file + https://github.com/materialdigital/core-ontology?tab=readme-ov-file Materials Science and Engineering Materials Science - 0.1.0 + 3.0.0-alpha1 - - CHIRO - CHEBI Integrated Role Ontology (CHIRO) - CHEBI provides a distinct role hierarchy. Chemicals in the structural hierarchy are connected via a 'has role' relation. CHIRO provides links from these roles to useful other classes in other ontologies. This will allow direct connection between chemical structures (small molecules, drugs) and what they do. This could be formalized using 'capable of', in the same way Uberon and the Cell Ontology link structures to processes. + + PSIMOD + Protein Modifications Ontology (PSIMOD) + PSI-MOD is an ontology developed by the Proteomics Standards Initiative (PSI) that describes protein chemical modifications, logically linked by an is_a relationship in such a way as to form a direct acyclic graph (DAG). The PSI-MOD ontology has more than 45 top-level nodes, and provides alternative hierarchical paths for classifying protein modifications either by the molecular structure of the modification, or by the amino acid residue that is modified. OWL - 2015-11-23 - Creative Commons 1.0 - https://terminology.tib.eu/ts/ontologies/chiro + 2022-06-13 + Creative Commons Attribution 4.0 + https://github.com/HUPO-PSI/psi-mod-CV Chemistry - Chemicals, Roles - 2015-11-23 + Protein Modifications + 1.031.6 - - SystemCapabilities - System Capabilities Ontology (SystemCapabilities) - This ontology describes system capabilities, operating ranges, and survival ranges. - W3C/OGC Spatial Data on the Web Working Group + + DISO + Dislocation Ontology (DISO) + DISO is an ontology that defines the linear defect, in particular dislocation concepts and relations between them in crystalline materials. + Ahmad Zainul Ihsan OWL - 2017-05-14 - W3C Software and Document License - https://terminology.tib.eu/ts/ontologies/SSNSYSTEM + 21.03.202 + Creative Commons Attribution 3.0 International (CC BY 3.0) + https://github.com/Materials-Data-Science-and-Informatics/dislocation-ontology Materials Science and Engineering - Materials Science, Engineering, Systems + Materials Science + 1.0 - - GFO - General Formal Ontology (GFO) - The General Formal Ontology is a top-level ontology for conceptual modeling, which is being constantly further developed by Onto-Med. It includes elaborations of categories like objects, processes, time and space, properties, relations, roles, functions, facts, and situations. Moreover, we are working on an integration with the notion of levels of reality in order to more appropriately capture entities in the material, mental, and social areas. + + NCIt + NCI Thesaurus (NCIt) + NCI Thesaurus (NCIt) is a reference terminology that includes broad coverage of the cancer domain, including cancer related diseases, findings and abnormalities. The NCIt OBO Edition aims to increase integration of the NCIt with OBO Library ontologies. NCIt OBO Edition releases should be considered experimental. OWL - 2024-11-18 + 2023-10-19 Creative Commons 4.0 - https://onto-med.github.io/GFO/release/2024-11-18/index-en.html - Upper Ontology + https://terminology.tib.eu/ts/ontologies/NCIT + Medicine + Cancer, Oncology + 24.04e - - EDAM - The ontology of data analysis and management (EDAM) - EDAM is a domain ontology of data analysis and data management in bio- and other sciences, and science-based applications. It comprises concepts related to analysis, modelling, optimisation, and data life cycle. Targetting usability by diverse users, the structure of EDAM is relatively simple, divided into 4 main sections: Topic, Operation, Data (incl. Identifier), and Format. - Federico Bianchini, Hervé Ménager, Jon Ison, Matúš Kalaš - OWL - 24.09.2024 - Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/edam - General Knowledge - General - 1.25-20240924T0027Z-unstable(1.26) + + MSLE + Material Science Lab Equipment Ontology (MSLE) + The current ontology describes Material Science Lab Equipment. + TTL + Sep 15, 2022 + https://github.com/MehrdadJalali-AI/MSLE-Ontology + Materials Science and Engineering + Materials Science + 1.1 - - UO - Units of Measurement Ontology (UO) - Metrical units for use in conjunction with PATO. - KAUST - OWL - 2023-05-25 - Creative Commons 3.0 - https://bioportal.bioontology.org/ontologies/UO - Units and Measurements + + AMOntology + Additive Manufacturing Ontology (AMOntology) + The AM ontology has been developed following two major milestones. The ontology developed within the first milestone includes AMProcessOntology, ModelOntology and AMOntology files. AMProcessOntology contains the set of entities used to capture knowledge about additive manufacturing processes. ModelOntology contains the set of entities used to capture knowledge about modeling concepts that represent (possibly) multi-physics multi-scale processes. AMOntology uses AMProcessOntology and ModelOntology files to describe entities that capture knowledge about characteristics of computational models for AM processes. + Iassou Souroko, Ali Riza Durmaz + TTL + 2023-05-10 + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/iassouroko/AMontology + Materials Science and Engineering + Manufacturing + 1.0 - - DOLCE - Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) - The Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) is a foundational ontology that provides a conceptual framework for the formalization of domain ontologies. - Laboratory for Applied Ontology, ISTC-CNR + + ICON + Icon Ontology (ICON) + The ICON ontology deals with high granularity art interpretation. It was developed by conceptualizing Panofsky's theory of levels of interpretation, therefore artworks can be described according to Pre-iconographical, Iconographical and Iconological information. + Knowledge Media Institute OWL + April 26th, 2024 Creative Commons 4.0 - https://www.loa.istc.cnr.it/index.php/dolce/ - Upper Ontology - Linguistics, Cognitive Science + https://w3id.org/icon/ontology/ + Arts and Humanities + Art History, Cultural Heritage + 2.1.0 - - MatVoc - Materials Vocabulary (MatVoc) - The official ontology produced in the context of the STREAM project. - Tatyana Sheveleva, Javad Chamanara - RDF - 2022-12-12 - MIT License - https://stream-project.github.io/#overv + + VIMMP + Virtual Materials Marketplace Ontologies (VIMMP) + The Virtual Materials Marketplace (VIMMP) project is developing an open platform for providing and accessing services related to materials modelling. Within VIMMP, a system of marketplace-level ontologies is developed to characterize services, models, and interactions between users; the European Materials and Modelling Ontology (EMMO, recently renamed while keeping the original acronym) is employed as a top-level ontology. The ontologies are used to annotate data that are stored in the ZONTAL Space component of VIMMP and to support the ingest and retrieval of data and metadata at the VIMMP marketplace front-end. + Ilian T. Todorov, Martin Thomas Horsch, Michael A. Seaton, Silvia Chiacchiera + OWL + 2021-01-02 + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://matportal.org/ontologies/VIMMP_ONTOLOGIES Materials Science and Engineering + Materials Modeling + + + Metadata4Ing + Metadata for Intelligent Engineering (Metadata4Ing) + The ontology Metadata4Ing provides a framework for the semantic description of research data and of the whole data generation process, embracing the object of investigation, all sample and data manipulation methods and tools, the data files themselves, and the roles of persons and institutions. The structure and application of the ontology are based on the principles of modularity and inheritance. + Metadata4Ing Workgroup + TTL + 2025-03-10 + Creative Commons 4.0 + https://git.rwth-aachen.de/nfdi4ing/metadata4ing/metadata4ing + Scholarly Knowledge Materials Science - 1.0.0 + 1.3.1 BFO @@ -1795,18 +1596,111 @@ Basic 2.0 - - GoodRelations - Good Relations Language Reference (GoodRelations) - GoodRelations is a standardized vocabulary (also known as "schema", "data dictionary", or "ontology") for product, price, store, and company data that can (1) be embedded into existing static and dynamic Web pages and that (2) can be processed by other computers. This increases the visibility of your products and services in the latest generation of search engines, recommender systems, and other novel applications. - Martin Hepp + + CDCO + Crystallographic Defect Core Ontology (CDCO) + CDCO defines the common terminology shared across all types of crystallographic defects, providing a unified framework for data integration in materials science. + https://orcid.org/0000-0001-7564-7990 + OWL + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/OCDO/cdco + Materials Science and Engineering + Materials Science + 1.0.0 + + + DOAP + The Description of a Project vocabulary (DOAP) + The Description of a Project vocabulary (DOAP), described using W3C RDF Schema and the Web Ontology Language to describe software projects, and in particular open source projects. + Edd Wilder-James + RDF + 2020-04-03 + Apache License 2.0 + https://github.com/ewilderj/doap/blob/master/schema/doap.rdf + Industry + Software + + + ChMO + Chemical Methods Ontology (ChMO) + The Chemical Methods Ontology contains more than 3000 classes and describes methods used to: - collect data in chemical experiments, such as mass spectrometry and electron microscopy. - prepare and separate material for further analysis, such as sample ionisation, chromatography, and electrophoresis - synthesise materials, such as epitaxy and continuous vapour deposition It also describes the instruments used in these experiments, such as mass spectrometers and chromatography columns and their outputs. + OWL + 2022-04-19 + Creative Commons 4.0 + https://github.com/rsc-ontologies/rsc-cmo + Chemistry + + + AgrO + Agronomy Ontology (AgrO) + An ontology is a formal representation of a disciplinary domain, representing a semantic standard that can be employed to annotate data where key concepts are defined, as well as the relationships that exist between those concepts (Gruber, 2009). Ontologies provide a common language for different kinds of data to be easily interpretable and interoperable allowing easier aggregation and analysis. The Agronomy Ontology (AgrO) provides terms from the agronomy domain that are semantically organized and can facilitate the collection, storage and use of agronomic data, enabling easy interpretation and reuse of the data by humans and machines alike. To fully understand the implications of varying practices within cropping systems and derive insights, it is often necessary to pull together information from data in different disciplinary domains. For example, data on field management, soil, weather and crop phenotypes may need to be aggregated to assess performance of particular crop under different management interventions. However, agronomic data are often collected, described, and stored in inconsistent ways, impeding data comparison, mining, interpretation reuse. The use of standards for metadata and data annotation play a key role in addressing these challenges. While the CG Core Metadata Schema provides a metadata standard to describe agricultural datasets, the Agronomy Ontology enables the description of agronomic data variables using standard terms. + The Crop Ontology Consortium + RDF + 2022-11-02 + Creative Commons 4.0 + https://agroportal.lirmm.fr/ontologies/AGRO?p=summary + Agriculture + Agronomy + 1.0 + + + OIEManufacturing + Open Innovation Environment Manufacturing (OIEManufacturing) + The manufacturing module populates the physicalistic perspective with manufacturing subclasses categorised according to modern applied physical sciences. + Adham Hashibon, Daniele Toti, Emanuele Ghedini, Georg J. Schmitz, Gerhard Goldbeck, Jesper Friis, Pierluigi Del Nostro + TTL + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/emmo-repo/OIE-Ontologies/ + Materials Science and Engineering + Materials + + + MFOEM + Mental Functioning Ontology of Emotions - Emotion Module (MFOEM) + The Mental Functioning Ontology - Emotion Module (MFOEM) aims to include all relevant aspects of affective phenomena including their bearers, the different types of emotions, moods, etc., their different parts and dimensions of variation, their facial and vocal expressions, and the role of emotions and affective phenomena in general in influencing human behavior.This class processes Mental Functioning Ontology of Emotions (MFOEM) using default behavior. + Swiss Centre for Affective Sciences & University at Buffalo + OWL + Creative Commons 3.0 + http://purl.obolibrary.org/obo/MFOEM.owl + Medicine + Emotion + + + PROV + PROV Ontology (PROV-O) + The PROV Ontology (PROV-O) expresses the PROV Data Model [PROV-DM] using the OWL2 Web Ontology Language (OWL2) [OWL2-OVERVIEW]. It provides a set of classes, properties, and restrictions that can be used to represent and interchange provenance information generated in different systems and under different contexts. It can also be specialized to create new classes and properties to model provenance information for different applications and domains. The PROV Document Overview describes the overall state of PROV, and should be read before other PROV documents. + OWL + 2013-04-30 + W3C Software License + https://terminology.tib.eu/ts/ontologies/PROV + General Knowledge + General + 2013-04-30 + + + EURIO + EUropean Research Information Ontology (EURIO) + EURIO (EUropean Research Information Ontology) conceptualises, formally encodes and makes available in an open, structured and machine-readable format data about resarch projects funded by the EU's framework programmes for research and innovation. + Publications Office of the European Commission + RDF + 2023-10-19 + Creative Commons 4.0 + https://op.europa.eu/de/web/eu-vocabularies/dataset/-/resource?uri=http://publications.europa.eu/resource/dataset/eurio + Scholarly Knowledge + Research Information + 2.4 + + + HPOnt + The Heat Pump Ontology (HPOnt) + The Heat Pump Ontology (HPOnt) aims to formalize and represent all the relevant information of Heat Pumps. The HPOnt has been developed as part of the REACT project which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 824395. + REACT project team OWL - 2011-10-01 - Creative Commons 3.0 - https://www.heppnetz.de/ontologies/goodrelations/v1 - Finance - E-commerce - 1.0 + Creative Commons 4.0 + https://react2020.github.io/REACT-ONTOLOGY/HPOnt/index-en.html/ + Materials Science and Engineering + Materials Science + 0.2 ChordOntology @@ -1821,149 +1715,239 @@ Musical Works 1.0 - - BBCCoreConcepts - BBC Core Concepts Ontology (BBCCoreConcepts) - The generic BBC ontology for people, places, events, organisations, themes which represent things that make sense across the BBC. This model is meant to be generic enough, and allow clients (domain experts) link their own concepts e.g., athletes or politicians using rdfs:sublClassOf the particular concept. - jeremy.tarling@bbc.co.uk, tom.hodgkinson@bbc.co.uk + + Photovoltaics + EMMO Domain Ontology for Photovoltaics (Photovoltaics) + This ontology is describing Perovskite solar cells. + Casper Welzel Andersen, Simon Clark TTL - 2019-11-21 - Creative Commons 4.0 - https://www.bbc.co.uk/ontologies/core-concepts-ontology - News and Media - Core Concepts - 1.30 + Creative Commons license Attribution 4.0 International (CC BY 4.0) + https://github.com/emmo-repo/domain-photovoltaics + Materials Science and Engineering + Materials Science + 0.0.1 - - MaterialInformation - Material Information Ontology (MaterialInformation) - The Material Information ontology is divided into smaller ontologies (partitions). The partitions are Environment, Geometry, Material Information, Manufacturing Process, Property, Substance, Unit Dimension, Structure, Equation and Physical Constant. - Toshihiro Ashino + + PLDO + Planar Defects Ontology (PLDO) + PLDO is an ontology designed to describe planar defects in crystalline materials, such as grain boundaries and stacking faults, with a focus on their atomic-scale structure and properties. + https://orcid.org/0000-0001-7564-7990 OWL - https://github.com/EngyNasr/MSE-Benchmark/blob/main/testCases/secondTestCase/MaterialInformation.owl + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/OCDO/pldo Materials Science and Engineering - Materials Science + Materials Defects + 1.0.0 - - OIECharacterisation - Open Innovation Environment Characterisation (OIECharacterisation) - EMMO-compliant, domain-level OIE ontology tackling the areas of characterization methods. - Daniele Toti, Gerhard Goldbeck, Pierluigi Del Nostro + + FoodOn + Food Ontology (FoodON) + FoodOn, the food ontology, contains vocabulary for naming food materials and their anatomical and taxonomic origins, from raw harvested food to processed food products, for humans and domesticated animals. It provides a neutral and ontology-driven standard for government agencies, industry, nonprofits and consumers to name and reference food products and their components throughout the food supply chain. + OWL + 2025-01-16 + Creative Commons 4.0 + http://purl.obolibrary.org/obo/foodon.owl + Agriculture + Diet, Metabolomics, and Nutrition + + + OIEMaterials + Open Innovation Environment Materials (OIEMaterials) + The materials module populates the physicalistic perspective with materials subclasses categorised according to modern applied physical sciences. + Adham Hashibon, Daniele Toti, Emanuele Ghedini, Georg J. Schmitz, Gerhard Goldbeck, Jesper Friis, Pierluigi Del Nostro TTL Creative Commons Attribution 4.0 International (CC BY 4.0) https://github.com/emmo-repo/OIE-Ontologies/ Materials Science and Engineering Materials - - PO - Plant Ontology (PO) - The Plant Ontology (PO) is a structured vocabulary and database resource that links plant anatomy, morphology and growth and development to plant genomics data. + + EMMOCrystallography + Crystallography Ontology (EMMOCrystallography) + A crystallography domain ontology based on EMMO and the CIF core dictionary. It is implemented as a formal language. + TTL + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/emmo-repo/domain-crystallography + Materials Science and Engineering + Crystallography + 0.0.1 + + + DSIM + Dislocation Simulation and Model Ontology (DSIM) + Dislocation simulation and model ontology (DSIM) is an ontology developed to model various concepts and relationships in the discrete dislocation dynamics domain and microscopy techniques used in the dislocation domain. The various concepts are the numerical representation of dislocation applied in the dislocation dynamic simulation and the pictorial concept of pixel applied in representing dislocation in the experimental image, eg., TEM image, SEM image, and FIM image. + Ahmad Zainul Ihsan + OWL + 17.08.2023 + Creative Commons Attribution 3.0 Unported (CC BY 3.0) + https://github.com/OCDO/DSIM + Materials Science and Engineering + Materials Science + 1.0 + + + OBI + Ontology for Biomedical Investigations (OBI) + The Ontology for Biomedical Investigations (OBI) helps you communicate clearly about scientific investigations by defining more than 2500 terms for assays, devices, objectives, and more. OWL + 2025-01-09 Creative Commons 4.0 - https://github.com/Planteome/plant-ontology - Agriculture - Plant Anatomy, Morphology, Growth and Development + https://github.com/obi-ontology/obi/tree/master + Medicine + Biomedical Investigations - - Framester - Framester Ontology (Framester) - Framester is a a frame-based ontological resource acting as a hub between linguistic resources such as FrameNet, WordNet, VerbNet, BabelNet, DBpedia, Yago, DOLCE-Zero, and leveraging this wealth of links to create an interoperable predicate space formalized according to frame semantics and semiotics. Framester uses WordNet and FrameNet at its core, expands it to other resources transitively, and represents them in a formal version of frame semantics. - Aldo Gangemi + + BIBFRAME + Bibliographic Framework Ontology (BIBFRAME) + The Bibframe vocabulary consists of RDF classes and properties used for the description of items cataloged principally by libraries, but may also be used to describe items cataloged by museums and archives. Classes include the three core classes - Work, Instance, and Item - in addition to many more classes to support description. Properties describe characteristics of the resource being described as well as relationships among resources. For example: one Work might be a "translation of" another Work; an Instance may be an "instance of" a particular Bibframe Work. Other properties describe attributes of Works and Instances. For example: the Bibframe property "subject" expresses an important attribute of a Work (what the Work is about), and the property "extent" (e.g. number of pages) expresses an attribute of an Instance. + United States, Library of Congress RDF - 19-04-2016 - Creative Commons 4.0 - http://150.146.207.114/lode/extract?url=http://ontologydesignpatterns.org/ont/framester/framester.owl - Scholarly Knowledge - Linguistics - 1.0 + 2022-10-03 + Creative Commons 1.0 + https://id.loc.gov/ontologies/bflc.html + Education + Library, Museums, Archives + 2.5.0 - - SWO - Software Ontology (SWO) - The Software Ontology (SWO) is a resource for describing software tools, their types, tasks, versions, provenance and associated data. It contains detailed information on licensing and formats as well as software applications themselves, mainly (but not limited) to the bioinformatics community. - Allyson Lister, Andy Brown, Duncan Hull, Helen Parkinson, James Malone, Jon Ison, Nandini Badarinarayan, Robert Stevens + + RXNO + Reaction Ontology (RXNO) + RXNO is the name reaction ontology. It contains more than 500 classes representing organic reactions such as the Diels–Alder cyclization. OWL - 2013-07-01 + 2021-12-16 Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/SWO - Scholarly Knowledge - Software - 1.0 + https://github.com/rsc-ontologies/rxno + Chemistry - - FoodOn - Food Ontology (FoodON) - FoodOn, the food ontology, contains vocabulary for naming food materials and their anatomical and taxonomic origins, from raw harvested food to processed food products, for humans and domesticated animals. It provides a neutral and ontology-driven standard for government agencies, industry, nonprofits and consumers to name and reference food products and their components throughout the food supply chain. - OWL - 2025-01-16 + + CopyrightOnto + Copyright Ontology (CopyrightOnto) + The Copyright Ontology tries to formalise the copyright domain as a way to facilitate automated (or computer-supported) copyright management through the whole content value chain, as it is shaped by copyright law. Therefore, it does not focus just on the last step, end-users permissions to consume content, like many rights languages and ontologies do. + Rhizomik + TTL + 2019-09 Creative Commons 4.0 - http://purl.obolibrary.org/obo/foodon.owl - Agriculture - Diet, Metabolomics, and Nutrition + https://rhizomik.net/ontologies/copyrightonto/ + Law + Legal Knowledge - - PMDco - The Platform MaterialDigital core ontology (PMDco) - The PMD Core Ontology (PMDco) is a comprehensive framework for representing knowledge that encompasses fundamental concepts from the domains of materials science and engineering (MSE). The PMDco has been designed as a mid-level ontology to establish a connection between specific MSE application ontologies and the domain neutral concepts found in established top-level ontologies. The primary goal of the PMDco is to promote interoperability between diverse domains. - Jannis Grundmann + + RO + Relation Ontology (RO) + The Relations Ontology (RO) is a collection of OWL relations (ObjectProperties) intended for use across a wide variety of biological ontologies. OWL - 2025-03-20 + 2024-04-24 + CC0 + http://purl.obolibrary.org/obo/ro.owl + General Knowledge + Relations + 2024-04-24 + + + QUDV + Quantities, Units, Dimensions and Values (QUDV) + The SysML QUDV (Quantities, Units, Dimensions and Values) modelLibrary is specified in a UML/SysML class/block diagram. In order to generalize its potential usage and alignment with other standardization efforts concerning quantities and units, it is of interest to verify that the QUDV model can also be represented in the form of an ontology using a formal ontology definition language. + SysML + OWL + 2009-10-30 + Apache License 2.0 + https://www.omgwiki.org/OMGSysML/doku.php?id=sysml-qudv:qudv_owl + Units and Measurements + 2009-10-30 + + + MO + Microscopy Ontology (MO) + The Microscopy Ontology (MO) extends the ontological framework of the PMDco. The MO facilitates semantic integration and the interoperable connection of diverse data sources from the fields of microscopy and microanalysis. Consequently, the MO paves the way for new, adaptable data applications and analyses across various experiments and studies + https://orcid.org/0000-0002-3717-7104,https://orcid.org/0000-0002-7094-5371 + TTL Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/materialdigital/core-ontology?tab=readme-ov-file - Materials Science and Engineering - Materials Science - 3.0.0-alpha1 + https://github.com/materialdigital/microscopy-ontology?tab=readme-ov-file + Biology and Life Sciences + Microscopy + 2.0 - - PPlan - Ontology for Provenance and Plans (P-Plan) - The Ontology for Provenance and Plans (P-Plan) is an extension of the PROV-O ontology [PROV-O] created to represent the plans that guided the execution of scientific processes. P-Plan describes how the plans are composed and their correspondence to provenance records that describe the execution itself. - http://www.isi.edu/~gil/ + + GIST + GIST Upper Ontology (GIST) + Gist is Semantic Arts' minimalist upper ontology for the enterprise. It is designed to have the maximum coverage of typical business ontology concepts with the fewest number of primitives and the least amount of ambiguity. + Semantic Arts + RDF + 2024-Feb-27 + Creative Commons 4.0 + https://semanticarts.com/gist + General Knowledge + Upper Ontology + 12.1.0 + + + GND + Gemeinsame Normdatei (GND) + GND stands for Gemeinsame Normdatei (Integrated Authority File) and offers a broad range of elements to describe authorities. The GND originates from the German library community and aims to solve the name ambiguity problem in the library world. + Alexander Haffner + RDF + 2024-08-26 + Creative Commons 1.0 + https://d-nb.info/standards/elementset/gnd + Library and Cultural Heritage + Authority Files + 1.2.0 + + + CCO + Common Core Ontologies (CCO) + The Common Core Ontologies (CCO) is a widely-used suite of eleven ontologies that consist of logically well-defined generic terms and relations among them reflecting entities across all domains of interest. + TTL + 2024-11-06 + BSD-3-Clause license + https://github.com/CommonCoreOntology/CommonCoreOntologies + General Knowledge + General + 2.0 + + + OntoCAPE + Ontology of Computer-Aided Process Engineering (OntoCAPE) + OntoCAPE is a large-scale ontology for the domain of Computer Aided Process Engineering (CAPE). Represented in a formal, machine-interpretable ontology language, OntoCAPE captures consensual knowledge of the process engineering domain in a generic way such that it can be reused and shared by groups of people and across software systems. On the basis of OntoCAPE, novel software support for various engineering activities can be developed; possible applications include the systematic management and retrieval of simulation models and design documents, electronic procurement of plant equipment, mathematical modeling, as well as the integration of design data from distributed sources. + RWTH Aachen University OWL - 2014-03-12 - Creative Commons 4.0 - https://vocab.linkeddata.es/p-plan/index.html - Scholarly Knowledge - 1.3 + GNU General Public License. + https://www.avt.rwth-aachen.de/cms/avt/forschung/sonstiges/software/~ipts/ontocape/?lidx=1 + Materials Science and Engineering + Manufacturing + 2.0 - - CHAMEO - Characterisation Methodology Domain Ontology (CHAMEO) - An ontology for materials characterization which represents the evolution of the CHADA template in an ontological form, allowing to generate FAIR documentation of Characterisation Experiments and that has been used as a basis for the development of a number of technique-specific or application-specific ontologies in the materials characterisation domain. CHAMEO has been used as a foundation for the definition of the new CHADA template during the CWA. - https://orcid.org/0000-0002-4181-2852, https://orcid.org/0000-0002-5174-8508, https://orcid.org/0000-0002-9668-6961 + + GPO + General Process Ontology (GPO) + Basically, this ontology aims to model processes. Processes are holistic perspective elements that transform inputs/educts (matter, energy, information) into output/products (matter, energy, information) with the help of tools (devices, algorithms). They can be decomposed into sub-processes and have predecessor and successor processes. + Simon Stier TTL - 2024-04-12 Creative Commons Attribution 4.0 International (CC BY 4.0) - https://github.com/emmo-repo/domain-characterisation-methodology + https://github.com/General-Process-Ontology/ontology Materials Science and Engineering Materials Science - 1.0.0 - - OPMW - Open Provenance Model for Workflows (OPMW) - The Open Provenance Model for Workflows (OPMW) is an ontology for describing workflow traces and their templates based on the Open Provenance Model. It has been designed as a profile for OPM, extending and reusing OPM's core ontologies OPMV (OPM-Vocabulary) and OPMO (OPM-Ontology). - http://delicias.dia.fi.upm.es/members/DGarijo/#me, http://www.isi.edu/~gil/ - OWL - 2014-12-22 - Creative Commons Attribution 2.0 Generic (CC BY 2.0) - https://www.opmw.org/model/OPMW_20141222/ - Scholarly Knowledge - Workflows - 3.1 + + BattINFO + Battery Interface Ontology (BattINFO) + BattINFO is a foundational resource for harmonizing battery knowledge representation and enhancing data interoperability. The primary objective is to provide the necessary tools to create FAIR (Findable, Accessible, Interoperable, Reusable) battery data that can be integrated into the Semantic Web. + TTL + https://github.com/BIG-MAP/BattINFO + Materials Science and Engineering + Materials Science - - NMRCV - Nuclear Magnetic Resonance Controlled Vocabulary (NMRCV) - This artefact is an MSI-approved controlled vocabulary primarily developed under COSMOS EU and PhenoMeNal EU governance. The nmrCV is supporting the nmrML XML format with standardized terms. nmrML is a vendor agnostic open access NMR raw data standard. Its primaly role is analogous to the mzCV for the PSI-approved mzML XML format. It uses BFO2.0 as its Top level. This CV was derived from two predecessors (The NMR CV from the David Wishart Group, developed by Joseph Cruz) and the MSI nmr CV developed by Daniel Schober at the EBI. This simple taxonomy of terms (no DL semantics used) serves the nuclear magnetic resonance markup language (nmrML) with meaningful descriptors to amend the nmrML xml file with CV terms. Metabolomics scientists are encouraged to use this CV to annotrate their raw and experimental context data, i.e. within nmrML. The approach to have an exchange syntax mixed of an xsd and CV stems from the PSI mzML effort. The reason to branch out from an xsd into a CV is, that in areas where the terminology is likely to change faster than the nmrML xsd could be updated and aligned, an externally and decentrallised maintained CV can accompensate for such dynamics in a more flexible way. A second reason for this set-up is that semantic validity of CV terms used in an nmrML XML instance (allowed CV terms, position/relation to each other, cardinality) can be validated by rule-based proprietary validators: By means of cardinality specifications and XPath expressions defined in an XML mapping file (an instances of the CvMappingRules.xsd ), one can define what ontology terms are allowed in a specific location of the data model. - Daniel Schober + + EFO + Experimental Factor Ontology (EFO) + The Experimental Factor Ontology (EFO) provides a systematic description of many experimental variables available in EBI databases, and for projects such as the GWAS catalog. It combines parts of several biological ontologies, such as UBERON anatomy, ChEBI chemical compounds, and Cell Ontology. The scope of EFO is to support the annotation, analysis and visualization of data handled by many groups at the EBI and as the core ontology for Open Targets. EFO is developed by the EMBL-EBI Samples, Phenotypes and Ontologies Team (SPOT). OWL - 2017-10-19 - Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/NMRCV - Chemistry - 1.1.0 + 2025-02-17 + Apache 2.0 + https://www.ebi.ac.uk/efo + Biology and Life Sciences + Biology + 3.75.0 ONTORULE @@ -1977,270 +1961,286 @@ Materials Science and Engineering Materials Science - - ChEBI - Chemical Entities of Biological Interest (ChEBI) - Chemical Entities of Biological Interest (ChEBI) is a dictionary of molecular entities focused on ‘small’ chemical compounds. The term ‘molecular entity’ refers to any constitutionally or isotopically distinct atom, molecule, ion, ion pair, radical, radical ion, complex, conformer, etc., identifiable as a separately distinguishable entity. The molecular entities in question are either products of nature or synthetic products used to intervene in the processes of living organisms. ChEBI incorporates an ontological classification, whereby the relationships between molecular entities or classes of entities and their parents and/or children are specified. + + OIECharacterisation + Open Innovation Environment Characterisation (OIECharacterisation) + EMMO-compliant, domain-level OIE ontology tackling the areas of characterization methods. + Daniele Toti, Gerhard Goldbeck, Pierluigi Del Nostro + TTL + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/emmo-repo/OIE-Ontologies/ + Materials Science and Engineering + Materials + + + REX + Physico-chemical process ontology (REX) + REX is an ontology of physico-chemical processes, i.e. physico-chemical changes occurring in course of time. REX includes both microscopic processes (involving molecular entities or subatomic particles) and macroscopic processes. Some biochemical processes from Gene Ontology (GO Biological process) can be described as instances of REX. + University of Warsaw OWL - 01/01/2025 + 2025-03-11 Creative Commons 4.0 - https://www.ebi.ac.uk/chebi/ + https://terminology.tib.eu/ts/ontologies/REX Chemistry - Chemical Entities - 239 + 1.0 - - AIISO - Academic Institution Internal Structure Ontology (AIISO) - The Academic Institution Internal Structure Ontology (AIISO) provides classes and properties to describe the internal organizational structure of an academic institution. AIISO is designed to work in partnership with Participation (http://purl.org/vocab/participation/schema), FOAF (http://xmlns.com/foaf/0.1/) and aiiso-roles (http://purl.org/vocab/aiiso-roles/schema) to describe the roles that people play within an institution. - Open University - RDF - 2008-05-14 - Creative Commons 4.0 - https://vocab.org/aiiso/ + + WiLD + Workflows in Linked Data (WiLD) + Ontology to describe Workflows in Linked Data. + Tobias Käfer + TTL + 2020-06-10 + DBpedia License + https://databus.dbpedia.org/ontologies/purl.org/wild--vocab/2020.06.10-210552 Scholarly Knowledge - Academic Institution + Materials Science + + + GoodRelations + Good Relations Language Reference (GoodRelations) + GoodRelations is a standardized vocabulary (also known as "schema", "data dictionary", or "ontology") for product, price, store, and company data that can (1) be embedded into existing static and dynamic Web pages and that (2) can be processed by other computers. This increases the visibility of your products and services in the latest generation of search engines, recommender systems, and other novel applications. + Martin Hepp + OWL + 2011-10-01 + Creative Commons 3.0 + https://www.heppnetz.de/ontologies/goodrelations/v1 + Finance + E-commerce 1.0 - - GO - Gene Ontology (GO) - The Gene Ontology (GO) Provides structured controlled vocabularies for the annotation of gene products with respect to their molecular function, cellular component, and biological role. + + MOLBRINELL + MatoLab Brinell Test Ontology (MOL_BRINELL) + An ontology for describing the Brinell hardness testing process, made in the Materials Open Lab Project. + Birgit Skrotzki, Hossein Beygi Nasrabadi, Philipp von Hartrott, Vinicius Carrillo Beber, Yue Chen + TTL + 05/05/2022 + https://matportal.org/ontologies/MOL_BRINELL + Materials Science and Engineering + Materials Testing + 0.1 + + + MechanicalTesting + Mechanical Testing Ontology (MechanicalTesting) + A domain ontology for mechanical testing based on EMMO. + Fraunhofer IWM OWL - 2024-11-03 + Creative Commons Attribution 4.0 International (CC BY 4.0) + https://github.com/emmo-repo/domain-mechanical-testing + Materials Science and Engineering + Mechanical Testing + 1.0.0 + + + SIO + Semanticscience Integrated Ontology (SIO) + The semanticscience integrated ontology (SIO) provides a simple, integrated upper level ontology (types, relations) for consistent knowledge representation across physical, processual and informational entities. This project provides foundational support for the Bio2RDF (http://bio2rdf.org) and SADI (http://sadiframework.org) projects. + M. Dumontier + OWL + 03/25/2024 Creative Commons 4.0 - https://geneontology.org/docs/download-ontology/ - Biology and Life Sciences - Molecular Biology, Genetics + https://bioportal.bioontology.org/ontologies/SIO + Upper Ontology + Basic + 1.59 - - CHEMINF - Chemical Information Ontology (CHEMINF) - The chemical information ontology (cheminf) describes information entities about chemical entities. It provides qualitative and quantitative attributes to richly describe chemicals. Includes terms for the descriptors commonly used in cheminformatics software applications and the algorithms which generate them. - Egon Willighagen, Nina Jeliazkova, Ola Spjuth, Valery Tkachenko + + CSO + Computer Science Ontology (CSO) + The Computer Science Ontology (CSO) is a large-scale ontology of research areas in computer science. It provides a comprehensive vocabulary of research topics in computing, organized in a hierarchical structure. This class processes the Computer Science Ontology (CSO) with custom hooks for: - Topic-based class detection - superTopicOf relationships - contributesTo relationships + Knowledge Media Institute, Open University OWL - Creative Commons 1.0 - https://terminology.tib.eu/ts/ontologies/CHEMINF - Chemistry - 2.1.0 + Creative Commons 4.0 + https://cso.kmi.open.ac.uk/home + Scholarly Knowledge + Computer Science + 3.4 - - LPBFO - Laser Powder Bed Fusion Ontology (LPBFO) - The LPBF Ontology can be used to describe the additive manufacturing of a component via Laser Powder Bed Fusion (LPBF) / Selective Laser Melting (SLM). The ontology builds on BFO2.0 and BWMD_mid and has been developed to be used in conjunction with the digital workflows provided by Fraunhofer IWM. If possible, the terminology within this ontology was used as provided by ISO/ASTM 52900:2015. Recently, classes relevant for Life Cycle Analysis (LCA) were added that enable sustainability assessment. - Fraunhofer IWM + + SEPIO + Scientific Evidence and Provenance Information Ontology (SEPIO) + The SEPIO ontology is in its early stages of development, undergoing iterative refinement as new requirements emerge and alignment with existing standards is explored. The SEPIO core file imports two files which can be resolved at the URLs below: IAO ontology-metadata import: https://raw.githubusercontent.com/monarch-initiative/SEPIO-ontology/master/src/ontology/imports/ontology-metadata.owl bfo mireot: https://raw.githubusercontent.com/monarch-initiative/SEPIO-ontology/master/src/ontology/mireots/bfo-mireot.owl OWL - 2022-09-20 + 2015-02-23 Creative Commons 4.0 - https://matportal.org/ontologies/LPBFO + https://terminology.tib.eu/ts/ontologies/SEPIO + Scholarly Knowledge + Scientific Evidence + + + MassSpectrometry + Mass Spectrometry Ontology (MassSpectrometry) + A structured controlled vocabulary for the annotation of experiments concerned with proteomics mass spectrometry. + Andreas Bertsch + OWL + 12:02:2025 + Creative Commons 4.0 + https://terminology.tib.eu/ts/ontologies/MS + Chemistry + Mass Spectrometry, Proteomics + + + NanoMine + NanoMine Ontology (NanoMine) + Polymer Nanocomposites based ontology which enable researchers to develop and test broad-reaching hypotheses about how inter-relationships between different materials processing methods and composition result in specific changes in material properties. + TTL + APACHE 2.0 + https://github.com/tetherless-world/nanomine-ontology Materials Science and Engineering Materials Science - 1.1.9 - - Conference - Conference Ontology (Conference) - The conference-ontology is a new self-contained ontology for modeling knowledge about conferences. The conference-ontology adopts the best ontology design practices (e.g., Ontology Design Patterns, ontology reuse and interlinking) and guarantees interoperability with SWC ontology and all other pertinent vocabularies. - Aldo Gangemi et al. + + DUO + Data Use Ontology (DUO) + DUO is an ontology which represent data use conditions. OWL - 2016/04/30 - Creative Commons 3.0 - http://www.scholarlydata.org/ontology/conference-ontology.owl - Events - Conferences - - - EURIO - EUropean Research Information Ontology (EURIO) - EURIO (EUropean Research Information Ontology) conceptualises, formally encodes and makes available in an open, structured and machine-readable format data about resarch projects funded by the EU's framework programmes for research and innovation. - Publications Office of the European Commission - RDF - 2023-10-19 + 2025-02-17 Creative Commons 4.0 - https://op.europa.eu/de/web/eu-vocabularies/dataset/-/resource?uri=http://publications.europa.eu/resource/dataset/eurio + https://terminology.tib.eu/ts/ontologies/DUO/ Scholarly Knowledge - Research Information - 2.4 + 1.0 - - BBCProvenance - BBC Provenance News Ontology (BBCProvenance) - An ontology to capture data about the provenance of data in an RDF Triple Store. This provenance is focused on the immediate providers and not the ultimate source, so for example, this would record that geodata was provided by the BBC Locator team, and not geonames. In the Linked Data Platform, this data is applied to contexts or named graphs. A named graph is, in effect, a 'fourth part' to a triple, hence the term 'quad store'. + + BBC + BBC Ontology (BBC) + The BBC ontology codifies the logic that connects web documents, BBC products and platforms for which content is available. Currently, there are 10 major products in Future Media which produce content for BBC online. The majority of those contain more products dedicated in thematic areas, for example Education propositions are part of the K&L (Knowledge and Learning) product portfolio. LinkedData@bbc.co.uk TTL 2012-12-01 Creative Commons 4.0 - https://www.bbc.co.uk/ontologies/provenance-ontology + https://www.bbc.co.uk/ontologies/bbc-ontology/ News and Media - Provenance - 1.9 + News + 1.37 - - Nomisma - Nomisma Ontology (Nomisma) - Nomisma Ontology is a collaborative project to provide stable digital representations of numismatic concepts according to the principles of Linked Open Data. These take the form of http URIs that provide access to the information about a concept in various formats. The project is a collaborative effort of the American Numismatic Society and the Institute for the Study of the Ancient World at New York University. - American Numismatic Society, Institute for the Study of the Ancient World - TTL - 2025-01-22 + + ATOL + Animal Trait Ontology for Livestock (ATOL) + ATOL (Animal Trait Ontology for Livestock) is an ontology of characteristics defining phenotypes of livestock in their environment (EOL). ATOL aims to: - provide a reference ontology of phenotypic traits of farm animals for the international scientific and educational - communities, farmers, etc.; - deliver this reference ontology in a language which can be used by computers in order to support database management, semantic analysis and modeling; - represent traits as generic as possible for livestock vertebrates; - make the ATOL ontology as operational as possible and closely related to measurement techniques; - structure the ontology in relation to animal production. + INRAE, France + OWL + May 11, 2020 Creative Commons 4.0 - https://www.dainst.org/forschung/projekte/noslug/2098 - Arts and Humanities - Numismatics + https://bioportal.bioontology.org/ontologies/ATOL + Agriculture + Animal Science + 6.0 - - OBOE - Extensible Observation Ontology (OBOE) - The Extensible Observation Ontology (OBOE) is a formal ontology for capturing the semantics of scientific observation and measurement. The ontology supports researchers to add detailed semantic annotations to scientific data, thereby clarifying the inherent meaning of scientific observations. - The Regents of the University of California - OWL - Creative Commons 3.0 - https://terminology.tib.eu/ts/ontologies/OBOE + + DCAT + Data Catalog Vocabulary (DCAT) + Data Catalog Vocabulary (DCAT) is an RDF vocabulary designed to facilitate interoperability between data catalogs published on the Web. This document defines the schema and provides examples for its use. DCAT enables a publisher to describe datasets and data services in a catalog using a standard model and vocabulary that facilitates the consumption and aggregation of metadata from multiple catalogs. This can increase the discoverability of datasets and data services. It also makes it possible to have a decentralized approach to publishing data catalogs and makes federated search for datasets across catalogs in multiple sites possible using the same query mechanism and structure. Aggregated DCAT metadata can serve as a manifest file as part of the digital preservation process. + Digital Enterprise Research Institute (DERI) + RDF + 22 August 2024 + W3C Document License + https://www.w3.org/TR/vocab-dcat-3/ Scholarly Knowledge - Scientific Observation - 1.2 + Data Catalogs + 3.0 - - BBCPolitics - BBC Politics News Ontology (BBCPolitics) - The Politics Ontology describes the concepts that occur in BBC politics news. - https://www.r4isstatic.com/ + + Wine + Wine Ontology (Wine) + A project to define an RDF style ontology for wines and the wine-industry + RDF + https://github.com/UCDavisLibrary/wine-ontology + Food and Beverage + Wine + + + BBCSport + BBC Sport Ontology (BBCSport) + The Sport Ontology is a simple lightweight ontology for publishing data about competitive sports events. The terms in this ontology allow data to be published about: The structure of sports tournaments as a series of eventsThe competing of agents in a competitionThe type of discipline a event involvesThe award associated with the competition and how received it...etc Whilst it originates in a specific BBC use case, the Sport Ontology should be applicable to a wide range of competitive sporting events data publishing use cases. Care has been taken to try and ensure interoperability with more general ontologies in use. In particular, it draws heavily upon the events ontology. + https://uk.linkedin.com/pub/jem-rayfield/27/b19/757, https://uk.linkedin.com/in/paulwilton, https://www.blockslabpillar.com, https://www.linkedin.com/in/tfgrahame, https://uk.linkedin.com/pub/stuart-williams/8/684/351, https://uk.linkedin.com/in/brianwmcbride TTL - 2014-01-06 Creative Commons 4.0 - https://www.bbc.co.uk/ontologies/politics-ontology + https://www.bbc.co.uk/ontologies/sport-ontology News and Media - Politics - 0.9 + Sport + 3.2 - - EFO - Experimental Factor Ontology (EFO) - The Experimental Factor Ontology (EFO) provides a systematic description of many experimental variables available in EBI databases, and for projects such as the GWAS catalog. It combines parts of several biological ontologies, such as UBERON anatomy, ChEBI chemical compounds, and Cell Ontology. The scope of EFO is to support the annotation, analysis and visualization of data handled by many groups at the EBI and as the core ontology for Open Targets. EFO is developed by the EMBL-EBI Samples, Phenotypes and Ontologies Team (SPOT). + + BioPAX + Biological Pathways Exchange (BioPAX) + BioPAX is a standard language that aims to enable integration, exchange, visualization and analysis of biological pathway data. Specifically, BioPAX supports data exchange between pathway data groups and thus reduces the complexity of interchange between data formats by providing an accepted standard format for pathway data. OWL - 2025-02-17 - Apache 2.0 - https://www.ebi.ac.uk/efo + 16 April 2015 + http://www.biopax.org/ Biology and Life Sciences - Biology - 3.75.0 - - - SIOC - Semantically-Interlinked Online Communities (SIOC) - The SIOC (Semantically-Interlinked Online Communities) Ontology is an ontology for describing the information in online communities. This includes sites that support online discussions, blogging, file sharing, photo sharing, social networking, etc. - Data Science Institute, NUI Galway - RDF - 2018/02/28 - Creative Commons 3.0 - http://rdfs.org/sioc/spec/ - Social Sciences - Social Networks - 1.36 - - - DoCO - Document Components Ontology (DoCO) - DoCO, the Document Components Ontology, is an OWL 2 DL ontology that provides a general-purpose structured vocabulary of document elements. DoCO has been designed as a general unifying ontological framework for describing different aspects related to the content of scientific and other scholarly texts. Its primary goal has been to improve the interoperability and shareability of academic documents (and related services) when multiple formats are actually used for their storage. - David Shotton and Silvio Peroni - RDF - 2015-07-03 - Creative Commons 4.0 - http://www.sparontologies.net/ontologies/doco - Education - document components - 1.3 - - - TimelineOntology - Timeline Ontology (TimelineOntology) - The Timeline Ontology is centered around the notion of timeline, seen here as a way to identify a temporal backbone. A timeline may support a signal, a video, a score, a work, etc. - Christopher Sutton, Yves Raimond, Matthias Mauch - RDF - 25th October 2007 - Creative Commons 1.0 - https://github.com/motools/timelineontology - Arts and Humanities - Music Theory + Bioinformatics 1.0 - - YAGO - YAGO Ontology (YAGO) - YAGO is a large semantic knowledge base, derived from Wikipedia, WordNet, and GeoNames. It contains knowledge about more than 10 million entities and contains more than 120 million facts about these entities. YAGO is special in several ways: It has a clean taxonomy, which was manually built, and it is the only knowledge base with such a large coverage, the clean taxonomy, and the extraction from Wikipedia, WordNet, and GeoNames. - Max Planck Institute for Informatics - TTL - April, 2024 - Creative Commons 3.0 - https://yago-knowledge.org/downloads/yago-4-5 - General Knowledge - People, Cities, Countries, Movies, Organizations - 4.5 - - - GeoNames - GeoNames Ontology (GeoNames) - The Geonames ontologies provides elements of description for geographical features, in particular those defined in the geonames.org database. - Bernard Vatant - RDF - 2022-01-30 - Creative Commons 3.0 - https://www.geonames.org/ontology - Geography - Geographic Knowledge - 3.3 + + PeriodicTable + Periodic Table of the Elements Ontology (PeriodicTable) + PeriodicTable.owl is a representation of the Periodic Table of the Elements in the OWL Web Ontology Language. It provides reference data to support Semantic Web applications in chemistry and related disciplines. + Michael Cook + OWL + 2004/02/05 + https://www.daml.org/2003/01/periodictable/ + Materials Science and Engineering + Periodic Table of Elements + 1.10 - - TribAIn - Tribology and Artificial Intelligence Ontology (TribAIn) - TribAIn is an ontology for the description of tribological experiments and their results. It is designed to be used in the context of the TribAIn project, which aims to develop a knowledge-based system for the design of tribological systems. - Patricia Kügler - TTL + + MOP + Molecular Process Ontology (MOP) + MOP is the molecular process ontology. It contains the molecular processes that underlie the name reaction ontology RXNO, for example cyclization, methylation and demethylation. + OWL + 2022-05-11 Creative Commons 4.0 - https://github.com/snow0815/tribAIn - Scholarly Knowledge + https://terminology.tib.eu/ts/ontologies/MOP + Chemistry + Chemistry, Molecular Biology + 2022-05-11 - - SEPIO - Scientific Evidence and Provenance Information Ontology (SEPIO) - The SEPIO ontology is in its early stages of development, undergoing iterative refinement as new requirements emerge and alignment with existing standards is explored. The SEPIO core file imports two files which can be resolved at the URLs below: IAO ontology-metadata import: https://raw.githubusercontent.com/monarch-initiative/SEPIO-ontology/master/src/ontology/imports/ontology-metadata.owl bfo mireot: https://raw.githubusercontent.com/monarch-initiative/SEPIO-ontology/master/src/ontology/mireots/bfo-mireot.owl + + FAIR + FAIR Vocabulary (FAIR) + This is the formal vocabulary (ontology) describing the FAIR principles. OWL - 2015-02-23 Creative Commons 4.0 - https://terminology.tib.eu/ts/ontologies/SEPIO - Scholarly Knowledge - Scientific Evidence + https://terminology.tib.eu/ts/ontologies/FAIR + Upper Ontology + Data, Metadata - - RXNO - Reaction Ontology (RXNO) - RXNO is the name reaction ontology. It contains more than 500 classes representing organic reactions such as the Diels–Alder cyclization. + + OntoKin + Chemical Kinetics Ontology (OntoKin) + OntoKin is an ontology developed for representing chemical kinetic reaction mechanisms. + IEEE OWL - 2021-12-16 + 08 February 2022 Creative Commons 4.0 - https://github.com/rsc-ontologies/rxno + https://www.ontologyportal.org/ Chemistry + 1.0 - - MechanicalTesting - Mechanical Testing Ontology (MechanicalTesting) - A domain ontology for mechanical testing based on EMMO. - Fraunhofer IWM + + IAO + Information Artifact Ontology (IAO) + The Information Artifact Ontology (IAO) is an ontology of information entities, originally driven by work by the OBI digital entity and realizable information entity branch. 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