OntoAligner 1.7.0

OntoAligner: A Comprehensive Modular and Robust Python Toolkit for Ontology Alignment.

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.

🧪 Installation

You can install OntoAligner from PyPI using pip:

pip install ontoaligner

Alternatively, to get the latest version directly from the source, use the following commands:

git clone [e-mail]:sciknoworg/OntoAligner.git
pip install ./ontoaligner

Next, verify the installation:

import ontoaligner

print(ontoaligner.__version__)

📚 Documentation

Comprehensive documentation for OntoAligner, including detailed guides and examples, is available at 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	📝 Code
Retrieval	📚 Retrieval Aligner	📝 Code
Large Language Models	📚 LLM Aligner	📝 Code
Retrieval Augmented Generation	📚 RAG Aligner	📝 Code
FewShot	📚 FewShot-RAG Aligner	📝 Code
In-Context Vectors Learning	📚 In-Context Vectors RAG	📝 Code
Knowledge Graph Embedding	📚 KGE Aligner	📝 Code
Property Alignment	📚 PropMatch Aligner	📝 Code
eCommerce	📚 Product Alignment in eCommerce	📝 Code

🚀 Quick Tour

Below is an example of using Retrieval-Augmented Generation (RAG) step-by-step approach for ontology matching:

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:

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 before getting started. You are also welcome to assist with the ongoing maintenance by referring to MAINTENANCE.md. Your support is greatly appreciated.

If you encounter any issues or have questions, please submit them in the GitHub issues tracker.

📚 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

  @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

  @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 .