doc fix and adding compatibility test CI/CD

.github/workflows/test-os-compatibility.yml

@@ -0,0 +1,49 @@
+name: Cross-platform Compatibility Tests
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs:
+  os-compatibility-tests:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, windows-latest, macos-latest]
+        python-version: ["3.10"]
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install Poetry
+        shell: bash
+        run: |
+          curl -sSL https://install.python-poetry.org | python -
+          echo "$HOME/.local/bin" >> $GITHUB_PATH
+          echo "$APPDATA/Python/Scripts" >> $GITHUB_PATH
+
+      - name: Configure Poetry and install plugin
+        shell: bash
+        run: |
+          poetry --version
+          poetry config virtualenvs.create false
+          poetry self add "poetry-dynamic-versioning[plugin]"
+
+      - name: Install dependencies
+        shell: bash
+        run: |
+          poetry install --no-interaction --no-ansi
+
+      - name: Run tests
+        shell: bash
+        run: |
+          poetry run pytest

docs/source/_static/custom.css

@@ -79,3 +79,39 @@
   margin-bottom: 1.2em;
   font-size: 1.1em;
 }
+
+.video-container {
+  position: relative;
+  width: 100%;
+  max-width: 960px;
+  margin: 2rem auto;
+  aspect-ratio: 16 / 9;
+}
+
+.video-container iframe {
+  width: 90%;
+  height: 90%;
+  border-radius: 12px;
+}
+
+.video-card {
+  max-width: 960px;
+  margin: 3rem auto;
+  padding: 1rem;
+  background: #f9fafb;
+  border-radius: 16px;
+  box-shadow: 0 10px 30px rgb(161, 159, 159);
+}
+
+.video-card iframe {
+  width: 100%;
+  aspect-ratio: 16 / 9;
+  border-radius: 12px;
+}
+
+.video-caption {
+  margin-top: 0.75rem;
+  text-align: center;
+  font-size: 1.05rem;
+  color: #0d3b5a;
+}

docs/source/aligner/kge.rst

@@ -34,8 +34,7 @@ Usage
 
 .. sidebar::
 
-    Full code is available at `OntoAligner Repository. <https://github.com/sciknoworg/OntoAligner/blob/main/examples/kge.py>`_
-
+    A usage example is available at `OntoAligner Repository. <https://github.com/sciknoworg/OntoAligner/blob/main/examples/kge.py>`_
 
 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.
 

docs/source/aligner/rag.rst

@@ -2,26 +2,37 @@ Retrieval-Augmented Generation
 ================================
 
 
-.. sidebar:: **Reference:**
-
-    `LLMs4OM: Matching Ontologies with Large Language Models <https://link.springer.com/chapter/10.1007/978-3-031-78952-6_3>`_
-
-	.. raw:: html
-
-		<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>
-
 LLMs4OM
 ----------------------------------
-**LLMs4OM: Matching Ontologies with Large Language Models**
 
-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.
+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.
 
 .. raw:: html
 
     <div align="center">
      <img src="https://raw.githubusercontent.com/sciknoworg/OntoAligner/refs/heads/dev/docs/source/img/LLMs4OM.jpg" width="80%"/>
     </div>
 
+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.
+
+.. raw:: html
+
+	<div class="video-card">
+	  <iframe
+	    src="https://videolectures.net/embed/videos/eswc2024_babaei_giglou_language_models?part=1"
+	    frameborder="0"
+	    allowfullscreen>
+	  </iframe>
+	  <p class="video-caption">
+	    ESWC 2024 Talk — LLMs4OM Presentation by Hamed Babaei Giglou.
+	  </p>
+	</div>
+
+
+.. note::
+
+    **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>`_
+
 
 Usage
 ----------------

docs/source/index.rst

@@ -17,11 +17,19 @@ OntoAligner was created by `Scientific Knowledge Organization (SciKnowOrg group)
       <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>
     </div>
 
-**Watch the OntoAligner presentation at EWC-2025.**
-
 .. raw:: html
 
-	<iframe src="https://videolectures.net/embed/videos/eswc2025_bernardin_babaei_giglu?part=1" width="100%" frameborder="0" allowfullscreen style="aspect-ratio:16/9"></iframe>
+	<div class="video-card">
+	  <iframe
+	    src="https://videolectures.net/embed/videos/eswc2025_bernardin_babaei_giglu?part=1"
+	    frameborder="0"
+	    allowfullscreen>
+	  </iframe>
+	  <p class="video-caption">
+	    ESWC 2025 Talk — OntoAligner Presentation by Hamed Babaei Giglou.
+	  </p>
+	</div>
+
 
 Citing
 =========