This repository contains a hands-on tutorial for ontology alignment using OntoAligner. It is designed for researchers, students, and practitioners who want to understand how OntoAligner works and how to use it in practice.
The tutorial is notebook-based and can be run directly in Google Colab 
| # | Notebook | Description | Open Notebook in Colab |
|---|---|---|---|
| 1 | 01-quick-introduction-to-ontoaligner.ipynb | Quick Introduction to OntoAligner: Overview of OntoAligner, basic concepts, and a simple end-to-end example | |
| 2 | 02-deep-dive-into-ontoaligner-modules-1.ipynb | Deep Dive into OntoAligner Modules – Part 1: Detailed explanation of core modules and internal components | |
| 3 | 03-deep-dive-into-ontoaligner-modules-2.ipynb | Deep Dive into OntoAligner Modules – Part 2: Detailed explanation of Retrieval, LLM, and RAG based Aligners | |
| 4 | 04-nested-ensemble-learning-aligners-in-ontoaligner.ipynb | Nested Ensemble learning Aligners in OntoAligner: Building group-level ensembles and combining them into one nested ensemble workflow | |
| 5 | 05-reusable-reranking-in-ontoaligner.ipynb | Reusable Reranking in OntoAligner: Applying reranking across retrieval, graph-based, flat-output, and RAG-style workflows | |
The easiest way to follow the tutorial is to open the notebooks in Google Colab
💻 Running locally: If you prefer to run the tutorial on your local machine, make sure you have:
- Python 3.10 or newer
- Jupyter Notebook or JupyterLab
- All required dependencies (listed in the notebooks)
🔧 Prerequisites: Basic familiarity with the following is recommended:
- Python
- Ontologies (e.g., OWL/RDF)
- Ontology alignment concepts (helpful but not required)
This tutorial is a work in progress. Contributions and feedback are welcome!