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<h3 align="center">OntoAligner: A Comprehensive Modular and Robust Python Toolkit for Ontology Alignment</h3>

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**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.


## 🧪 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
```

## 📚 Documentation

Comprehensive documentation for OntoAligner, including detailed guides and examples, is available at **[ontoaligner.readthedocs.io](https://ontoaligner.readthedocs.io/)**.

**Tutorials**

| Example                        | Tutorial                                                                                                        |                                            Script                                             |
|:-------------------------------|:----------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------:|
| Lightweight                    | [📚 Fuzzy Matching](https://ontoaligner.readthedocs.io/tutorials/lightweight.html)                              |   [📝 Code](https://github.com/sciknoworg/OntoAligner/blob/main/examples/fuzzy_matching.py)   |
| Retrieval                      | [📚 Retrieval Aligner](https://ontoaligner.readthedocs.io/tutorials/retriever.html)                             | [📝 Code](https://github.com/sciknoworg/OntoAligner/blob/main/examples/retriever_matching.py) |
| Large Language Models          | [📚 Large Language Models Aligner](https://ontoaligner.readthedocs.io/tutorials/llm.html)                       |    [📝 Code](https://github.com/sciknoworg/OntoAligner/blob/main/examples/llm_matching.py)    |
| Retrieval Augmented Generation | [📚 Retrieval Augmented Generation](https://ontoaligner.readthedocs.io/tutorials/rag.html)                      |       [📝 Code](https://github.com/sciknoworg/OntoAligner/blob/main/examples/rag_matching.py)|
| FewShot                        | [📚 FewShot RAG](https://ontoaligner.readthedocs.io/tutorials/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/tutorials/rag.html#in-context-vectors-rag)                  |       [📝 Code](https://github.com/sciknoworg/OntoAligner/blob/main/examples/icv_rag_matching.py)

## 🚀 Quick Tour

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

```python
import json
from ontoaligner.ontology import MaterialInformationMatOntoOMDataset
from ontoaligner.utils import metrics, xmlify
from ontoaligner.ontology_matchers 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)
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)
with open("matchings.xml", "w", encoding="utf-8") as xml_file:
    xml_file.write(xml_str)
```


## ⭐ Contribution

We welcome contributions to enhance OntoAligner and make it even better! Please review our contribution guidelines in [CONTRIBUTING.md](CONTRIBUTING.md) before getting started. Your support is greatly appreciated.

[//]: # (## 📧 Contact)

If you encounter any issues or have questions, please submit them in the [GitHub issues tracker](https://github.com/sciknoworg/OntoAligner/issues).


## 💡 Acknowledgements

If you use OntoAligner in your work or research, please cite the following:

```bibtex
@software{babaei_giglou_ontoaligner_2024,
  author       = {Hamed Babaei Giglou and Jennifer D'Souza and Oliver Karras and S{"o}ren Auer},
  title        = {OntoAligner: A Comprehensive Modular and Robust Python Toolkit for Ontology Alignment},
  version      = {1.0.0},
  year         = {2024},
  url          = {https://github.com/HamedBabaei/OntoAligner},
}
```

<p>
  This software is licensed under the
  <a href="https://opensource.org/licenses/MIT" target="_blank">MIT License</a>.
</p>
<a href="https://opensource.org/licenses/MIT" target="_blank">
  <img src="https://img.shields.io/badge/License-MIT-blue.svg" alt="MIT License">
</a>
