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📝 Doc fix & ✨ added compatibility test CI/CD (PR #72)
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‎.github/workflows/test-os-compatibility.yml‎

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name: Cross-platform Compatibility Tests
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on:
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push:
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branches: [main]
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pull_request:
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branches: [main]
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jobs:
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os-compatibility-tests:
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runs-on: ${{ matrix.os }}
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strategy:
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fail-fast: false
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matrix:
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os: [ubuntu-latest, windows-latest, macos-latest]
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python-version: ["3.10"]
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steps:
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- name: Checkout code
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uses: actions/checkout@v4
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v5
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install Poetry
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shell: bash
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run: |
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curl -sSL https://install.python-poetry.org | python -
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echo "$HOME/.local/bin" >> $GITHUB_PATH
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echo "$APPDATA/Python/Scripts" >> $GITHUB_PATH
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- name: Configure Poetry and install plugin
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shell: bash
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run: |
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poetry --version
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poetry config virtualenvs.create false
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poetry self add "poetry-dynamic-versioning[plugin]"
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- name: Install dependencies
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shell: bash
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run: |
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poetry install --no-interaction --no-ansi
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- name: Run tests
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shell: bash
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run: |
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poetry run pytest

‎docs/source/_static/custom.css‎

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margin-bottom: 1.2em;
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font-size: 1.1em;
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}
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.video-container {
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position: relative;
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width: 100%;
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max-width: 960px;
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margin: 2rem auto;
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aspect-ratio: 16 / 9;
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}
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.video-container iframe {
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width: 90%;
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height: 90%;
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border-radius: 12px;
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}
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.video-card {
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max-width: 960px;
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margin: 3rem auto;
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padding: 1rem;
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background: #f9fafb;
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border-radius: 16px;
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box-shadow: 0 10px 30px rgb(161, 159, 159);
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}
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.video-card iframe {
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width: 100%;
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aspect-ratio: 16 / 9;
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border-radius: 12px;
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}
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.video-caption {
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margin-top: 0.75rem;
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text-align: center;
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font-size: 1.05rem;
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color: #0d3b5a;
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}

‎docs/source/aligner/kge.rst‎

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.. sidebar::
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Full code is available at `OntoAligner Repository. <https://github.com/sciknoworg/OntoAligner/blob/main/examples/kge.py>`_
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A usage example is available at `OntoAligner Repository. <https://github.com/sciknoworg/OntoAligner/blob/main/examples/kge.py>`_
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This module guides you through a step-by-step process for performing ontology alignment using a KGEs and the OntoAligner library. By the end, you’ll understand how to preprocess data, encode ontologies, generate alignments, evaluate results, and save the outputs in XML and JSON formats.
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‎docs/source/aligner/rag.rst‎

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================================
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.. sidebar:: **Reference:**
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`LLMs4OM: Matching Ontologies with Large Language Models <https://link.springer.com/chapter/10.1007/978-3-031-78952-6_3>`_
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.. raw:: html
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<iframe src="https://videolectures.net/embed/videos/eswc2024_babaei_giglou_language_models?part=1" width="100%" frameborder="0" allowfullscreen style="aspect-ratio:16/9"></iframe>
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LLMs4OM
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----------------------------------
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**LLMs4OM: Matching Ontologies with Large Language Models**
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The retrieval augmented generation (RAG) module at OntoAligner is driven by a ``LLMs4OM`` framework, a novel approach for effective ontology alignment using LLMs. This framework utilizes two modules for retrieval and matching, respectively, enhanced by zero-shot prompting across three ontology representations: concept, concept-parent, and concept-children. The ``LLMs4OM`` framework, can match and even surpass the performance of traditional OM systems, particularly in complex matching scenarios. The ``LLMs4OM`` framework (as shown in the following diagram) offers a RAG approach within LLMs for OM. LLMs4OM uses :math:`O_{source}` as query :math:`Q(O_{source})` to retrieve possible matches for for any :math:`C_s \in C_{source}` from :math:`C_{target} \in O_{target}`. Where, :math:`C_{target}` is stored in the knowledge base :math:`KB(O_{target})`. Later, :math:`C_{s}` and obtained :math:`C_t \in C_{target}` are used to query the LLM to check whether the :math:`(C_s, C_t)` pair is a match. As shown in above diagram, the framework comprises four main steps: 1) Concept representation, 2) Retriever model, 3) LLM, and 4) Post-processing. But within the OntoAligner we we adapted the workflow into a parser, encoder, alignment, post-processing, evaluate, and export steps.
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The **LLMs4OM: Matching Ontologies with Large Language Models** work introduces a RAG approach for OA. The retrieval augmented generation (RAG) module at OntoAligner is driven by a ``LLMs4OM`` framework, a novel approach for effective ontology alignment using LLMs. This framework utilizes two modules for retrieval and matching, respectively, enhanced by zero-shot prompting across three ontology representations: concept, concept-parent, and concept-children. The ``LLMs4OM`` framework, can match and even surpass the performance of traditional OM systems, particularly in complex matching scenarios. The ``LLMs4OM`` framework is presented in the following diagram.
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.. raw:: html
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<div align="center">
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<img src="https://raw.githubusercontent.com/sciknoworg/OntoAligner/refs/heads/dev/docs/source/img/LLMs4OM.jpg" width="80%"/>
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</div>
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This offers a RAG approach within LLMs for OM. LLMs4OM uses :math:`O_{source}` as query :math:`Q(O_{source})` to retrieve possible matches for for any :math:`C_s \in C_{source}` from :math:`C_{target} \in O_{target}`. Where, :math:`C_{target}` is stored in the knowledge base :math:`KB(O_{target})`. Later, :math:`C_{s}` and obtained :math:`C_t \in C_{target}` are used to query the LLM to check whether the :math:`(C_s, C_t)` pair is a match. As shown in above diagram, the framework comprises four main steps: 1) Concept representation, 2) Retriever model, 3) LLM, and 4) Post-processing. But within the OntoAligner we we adapted the workflow into a parser, encoder, alignment, post-processing, evaluate, and export steps.
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.. raw:: html
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<div class="video-card">
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<iframe
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src="https://videolectures.net/embed/videos/eswc2024_babaei_giglou_language_models?part=1"
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frameborder="0"
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allowfullscreen>
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</iframe>
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<p class="video-caption">
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ESWC 2024 Talk — LLMs4OM Presentation by Hamed Babaei Giglou.
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</p>
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</div>
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.. note::
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**Reference:** Babaei Giglou, H., D’Souza, J., Engel, F., Auer, S. (2025). LLMs4OM: Matching Ontologies with Large Language Models. In: Meroño Peñuela, A., et al. The Semantic Web: ESWC 2024 Satellite Events. ESWC 2024. Lecture Notes in Computer Science, vol 15344. Springer, Cham. `https://doi.org/10.1007/978-3-031-78952-6_3 <https://doi.org/10.1007/978-3-031-78952-6_3>`_
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Usage
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----------------

‎docs/source/index.rst‎

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<strong>The vision is to create a unified hub that brings together a wide range of ontology alignment models, making integration seamless for researchers and practitioners.</strong>
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</div>
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**Watch the OntoAligner presentation at EWC-2025.**
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.. raw:: html
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<iframe src="https://videolectures.net/embed/videos/eswc2025_bernardin_babaei_giglu?part=1" width="100%" frameborder="0" allowfullscreen style="aspect-ratio:16/9"></iframe>
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<div class="video-card">
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<iframe
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src="https://videolectures.net/embed/videos/eswc2025_bernardin_babaei_giglu?part=1"
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frameborder="0"
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allowfullscreen>
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</iframe>
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<p class="video-caption">
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ESWC 2025 Talk — OntoAligner Presentation by Hamed Babaei Giglou.
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</p>
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</div>
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Citing
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=========

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