https://github.com/sciknoworg/OntoAligner
OntoAligner: A Python Toolkit for Ontology Alignment https://pypi.org/project/OntoAligner/
https://github.com/sciknoworg/OntoAligner
large-language-models ontology ontology-alignment ontology-engineering ontology-matching python-library
Last synced: 25 days ago
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OntoAligner: A Python Toolkit for Ontology Alignment https://pypi.org/project/OntoAligner/
- Host: GitHub
- URL: https://github.com/sciknoworg/OntoAligner
- Owner: sciknoworg
- License: apache-2.0
- Created: 2024-10-01T15:58:53.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2026-03-22T11:42:53.000Z (3 months ago)
- Last Synced: 2026-03-28T16:05:59.460Z (2 months ago)
- Topics: large-language-models, ontology, ontology-alignment, ontology-engineering, ontology-matching, python-library
- Language: Python
- Homepage: https://ontoaligner.readthedocs.io
- Size: 2.27 MB
- Stars: 66
- Watchers: 5
- Forks: 8
- Open Issues: 17
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
Awesome Lists containing this project
- awesome-ontology - OntoAligner
README
[](https://badge.fury.io/py/OntoAligner)
[](https://pepy.tech/projects/ontoaligner)
[](https://github.com/pre-commit/pre-commit)
[](https://ontoaligner.readthedocs.io/)
[](MAINTANANCE.md)
[](https://doi.org/10.5281/zenodo.14533133)
OntoAligner: A Comprehensive Modular and Robust Python Toolkit for Ontology Alignment
**OntoAligner** is a Python library designed to simplify ontology alignment and matching for researchers, practitioners, and developers. With a modular architecture and robust features, OntoAligner provides powerful tools to bridge ontologies effectively.
📘 **Explore the OntoAligner tutorial [here](tutorial/README.md).**
## 🧪 Installation
You can install **OntoAligner** from PyPI using `pip`:
```bash
pip install ontoaligner
```
Alternatively, to get the latest version directly from the source, use the following commands:
```bash
git clone git@github.com:sciknoworg/OntoAligner.git
pip install ./ontoaligner
```
Next, verify the installation:
```python
import ontoaligner
print(ontoaligner.__version__)
```
## 📚 Documentation
Comprehensive documentation for OntoAligner, including detailed guides and examples, is available at **[ontoaligner.readthedocs.io](https://ontoaligner.readthedocs.io/)**. Below are some key tutorials with links to both the documentation and the corresponding example codes.
| Example | Tutorial | Script |
|:-------------------------------|:--------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------:|
| Lightweight | [📚 Fuzzy Matching](https://ontoaligner.readthedocs.io/aligner/lightweight.html) | [📝 Code](https://github.com/sciknoworg/OntoAligner/blob/main/examples/fuzzy_matching.py) |
| Retrieval | [📚 Retrieval Aligner](https://ontoaligner.readthedocs.io/aligner/retriever.html) | [📝 Code](https://github.com/sciknoworg/OntoAligner/blob/main/examples/retriever_matching.py) |
| Large Language Models | [📚 LLM Aligner](https://ontoaligner.readthedocs.io/aligner/llm.html) | [📝 Code](https://github.com/sciknoworg/OntoAligner/blob/main/examples/llm_matching.py) |
| Retrieval Augmented Generation | [📚 RAG Aligner](https://ontoaligner.readthedocs.io/aligner/rag.html) | [📝 Code](https://github.com/sciknoworg/OntoAligner/blob/main/examples/rag_matching.py)|
| FewShot | [📚 FewShot-RAG Aligner](https://ontoaligner.readthedocs.io/aligner/rag.html#fewshot-rag) | [📝 Code](https://github.com/sciknoworg/OntoAligner/blob/main/examples/rag_matching.py)
| In-Context Vectors Learning | [📚 In-Context Vectors RAG](https://ontoaligner.readthedocs.io/aligner/rag.html#in-context-vectors-rag) | [📝 Code](https://github.com/sciknoworg/OntoAligner/blob/main/examples/icv_rag_matching.py)
| Knowledge Graph Embedding | [📚 KGE Aligner](https://ontoaligner.readthedocs.io/aligner/kge.html) | [📝 Code](https://github.com/sciknoworg/OntoAligner/blob/main/examples/kge.py)
| Property Alignment | [📚 PropMatch Aligner](https://ontoaligner.readthedocs.io/aligner/propmatch.html) | [📝 Code](https://github.com/sciknoworg/OntoAligner/blob/main/examples/property_alignment)
| eCommerce | [📚 Product Alignment in eCommerce](https://ontoaligner.readthedocs.io/usecases/ecommerce.html) | [📝 Code](https://github.com/sciknoworg/OntoAligner/blob/dev/examples/ecommerce_product_alignment.py)
## 🚀 Quick Tour
Below is an example of using Retrieval-Augmented Generation (RAG) step-by-step approach for ontology matching:
```python
from ontoaligner.ontology import MaterialInformationMatOntoOMDataset
from ontoaligner.utils import metrics, xmlify
from ontoaligner.aligner import MistralLLMBERTRetrieverRAG
from ontoaligner.encoder import ConceptParentRAGEncoder
from ontoaligner.postprocess import rag_hybrid_postprocessor
# Step 1: Initialize the dataset object for MaterialInformation MatOnto dataset
task = MaterialInformationMatOntoOMDataset()
print("Test Task:", task)
# Step 2: Load source and target ontologies along with reference matchings
dataset = task.collect(
source_ontology_path="assets/MI-MatOnto/mi_ontology.xml",
target_ontology_path="assets/MI-MatOnto/matonto_ontology.xml",
reference_matching_path="assets/MI-MatOnto/matchings.xml"
)
# Step 3: Encode the source and target ontologies
encoder_model = ConceptParentRAGEncoder()
encoded_ontology = encoder_model(source=dataset['source'], target=dataset['target'])
# Step 4: Define configuration for retriever and LLM
retriever_config = {"device": 'cuda', "top_k": 5}
llm_config = {"device": "cuda", "max_length": 300, "max_new_tokens": 10, "batch_size": 15}
# Step 5: Initialize Generate predictions using RAG-based ontology matcher
model = MistralLLMBERTRetrieverRAG(retriever_config=retriever_config, llm_config=llm_config)
model.load(llm_path = "mistralai/Mistral-7B-v0.3", ir_path="all-MiniLM-L6-v2")
predicts = model.generate(input_data=encoded_ontology)
# Step 6: Apply hybrid postprocessing
hybrid_matchings, hybrid_configs = rag_hybrid_postprocessor(predicts=predicts,
ir_score_threshold=0.1,
llm_confidence_th=0.8)
evaluation = metrics.evaluation_report(predicts=hybrid_matchings, references=dataset['reference'])
print("Hybrid Matching Evaluation Report:", evaluation)
# Step 7: Convert matchings to XML format and save the XML representation
xml_str = xmlify.xml_alignment_generator(matchings=hybrid_matchings)
open("matchings.xml", "w", encoding="utf-8").write(xml_str)
```
Ontology alignment pipeline using RAG method:
```python
import ontoaligner
pipeline = ontoaligner.OntoAlignerPipeline(
task_class=ontoaligner.ontology.MouseHumanOMDataset,
source_ontology_path="assets/MI-MatOnto/mi_ontology.xml",
target_ontology_path="assets/MI-MatOnto/matonto_ontology.xml",
reference_matching_path="assets/MI-MatOnto/matchings.xml"
)
matchings, evaluation = pipeline(
method="rag",
encoder_model=ontoaligner.encoder.ConceptRAGEncoder(),
model_class=ontoaligner.aligner.MistralLLMBERTRetrieverRAG,
postprocessor=ontoaligner.postprocess.rag_hybrid_postprocessor,
llm_path='mistralai/Mistral-7B-v0.3',
retriever_path='all-MiniLM-L6-v2',
llm_threshold=0.5,
ir_rag_threshold=0.7,
top_k=5,
max_length=512,
max_new_tokens=10,
device='cuda',
batch_size=32,
return_matching=True,
evaluate=True
)
print("Matching Evaluation Report:", evaluation)
```
## 👥 Contact & Contributions
We welcome contributions to enhance OntoAligner and make it even better! Please review our contribution guidelines in [CONTRIBUTING.md](CONTRIBUTING.md) before getting started. You are also welcome to assist with the ongoing maintenance by referring to [MAINTENANCE.md](MAINTENANCE.md). Your support is greatly appreciated.
If you encounter any issues or have questions, please submit them in the [GitHub issues tracker](https://github.com/sciknoworg/OntoAligner/issues).
## 📚 Citing this Work
If you use OntoAligner in your work or research, please cite the following preprint:
- OntoAligner Library:
> Babaei Giglou, H., D’Souza, J., Karras, O., Auer, S. (2025). OntoAligner: A Comprehensive Modular and Robust Python Toolkit for Ontology Alignment. In: Curry, E., et al. The Semantic Web. ESWC 2025. Lecture Notes in Computer Science, vol 15719. Springer, Cham. https://doi.org/10.1007/978-3-031-94578-6_10
📌 BibTeX
```bibtex
@InProceedings{10.1007/978-3-031-94578-6_10,
author="Babaei Giglou, Hamed and D'Souza, Jennifer and Karras, Oliver and Auer, S{\"o}ren",
editor="Curry, Edward and Acosta, Maribel and Poveda-Villal{\'o}n, Maria and van Erp, Marieke and Ojo, Adegboyega and Hose, Katja and Shimizu, Cogan and Lisena, Pasquale",
title="OntoAligner: A Comprehensive Modular and Robust Python Toolkit for Ontology Alignment",
booktitle="The Semantic Web",
year="2025",
publisher="Springer Nature Switzerland",
address="Cham",
pages="174--191"
}
```
- LLMs4OM (for RAG module)
> 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
📌 BibTeX
```bibtex
@InProceedings{10.1007/978-3-031-78952-6_3,
author="Babaei Giglou, Hamed and D'Souza, Jennifer and Engel, Felix and Auer, S{\"o}ren",
editor="Mero{\~{n}}o Pe{\~{n}}uela, Albert and Corcho, Oscar and Groth, Paul and Simperl, Elena and Tamma, Valentina and Nuzzolese, Andrea Giovanni and Poveda-Villal{\'o}n, Maria and Sabou, Marta and Presutti, Valentina and Celino, Irene and Revenko, Artem and Raad, Joe and Sartini, Bruno and Lisena, Pasquale",
title="LLMs4OM: Matching Ontologies with Large Language Models",
booktitle="The Semantic Web: ESWC 2024 Satellite Events",
year="2025",
publisher="Springer Nature Switzerland",
address="Cham",
pages="25--35",
isbn="978-3-031-78952-6"
}
```
## 📃 License
This software is licensed under [](https://opensource.org/licenses/Apache-2.0).
[//]: # (This work is licensed under a MIT License)
[//]: # (is archived in Zenodo under the DOI [](https://doi.org/10.5281/zenodo.14533133) and )