OntoLearner 1.1.0

OntoLearner: A Modular Python Library for Ontology Learning with LLMs.

OntoLearner: A Modular Python Library for Ontology Learning with LLMs

OntoLearner is a modular and extensible architecture designed to support ontology learning and reuse. The conceptual and functional architecture of OntoLearner is shown as following. The framework comprises three core components—Ontologizers, Learning Tasks, and Learner Models—structured to enable reusable and customizable ontology engineering workflows.

🧪 Installation

OntoLearner is available on PyPI and you can install using pip:

pip install ontolearner

Next, verify the installation:

import ontolearner

print(ontolearner.__version__)

🔗 Essential Resources

Resource	Info
📚 OntoLearner Documentation	Dive into OntoLearner's extensive documentation to explore its modular architecture, including Ontologizers, Learning Tasks, and Learner Models. The documentation provides detailed guides, references, and tutorials to help you get started and make the most of OntoLearner's capabilities.
🤗 Datasets on Hugging Face	You can access the curated colloctions of machine-readable ontologies across diverse domains such as agriculture, medicine, social sciences, and more. OntoLearner Benchmarking datasets are optimized for integration into generative AI pipelines, supporting versioning, streaming, and metadata inspection.

🚀 Quick Tour

Get started with OntoLearner in just a few lines of code. This guide demonstrates how to initialize ontologies, load datasets, and train an LLM-assisted learner for ontology engineering tasks.

Basic Usage:

from ontolearner.ontology import Wine

# 1. Initialize an ontologizer from OntoLearner
ontology = Wine()

# 2. Load the ontology automatically from Hugging Face
ontology.load()

# 3. Extract the learning task dataset
data = ontology.extract()

LLM-Based Learning Pipeline:

from ontolearner import ontology, utils, learner

# 1. Load the ontology and extract training data
onto = ontology.Wine()
data = onto.extract()

# 2. Split into train and test sets
train_data, test_data = utils.train_test_split(
    data, test_size=0.2, random_state=42
)

# 3. Initialize a Retrieval-Augmented Generation (RAG) learner
retriever = learner.BERTRetrieverLearner()
llm = learner.AutoLearnerLLM()
prompt = learner.StandardizedPrompting(task="term-typing")

rag_learner = learner.AutoRAGLearner(
    learner_retriever=retriever,
    learner_llm=llm,
    prompting=prompt
)

# 4. Load pretrained components
rag_learner.load(
    retriever_id="sentence-transformers/all-MiniLM-L6-v2",
    llm_id="mistralai/Mistral-7B-Instruct-v0.1"
)

# 5. Fit the model to training data
rag_learner.fit(train_data=train_data, task="term-typing")

# 6. Predict on test data
predicted = rag_learner.predict(test_data, task="term-typing")

⭐ Contribution

We welcome contributions to enhance OntoLearner 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.

💡 Acknowledgements

If you find this repository helpful or use OntoLearner in your work or research, feel free to cite our publication:

@inproceedings{babaei2023llms4ol,
  title={LLMs4OL: Large language models for ontology learning},
  author={Babaei Giglou, Hamed and D’Souza, Jennifer and Auer, S{\"o}ren},
  booktitle={International Semantic Web Conference},
  pages={408--427},
  year={2023},
  organization={Springer}
}

or:

@software{babaei_giglou_2025_15399783,
  author       = {Babaei Giglou, Hamed and D'Souza, Jennifer and Aioanei, Andrei and Mihindukulasooriya, Nandana and Auer, Sören},
  title        = {OntoLearner: A Modular Python Library for Ontology Learning with LLMs},
  month        = may,
  year         = 2025,
  publisher    = {Zenodo},
  version      = {v1.0.1},
  doi          = {10.5281/zenodo.15399783},
  url          = {https://doi.org/10.5281/zenodo.15399783},
}

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This software is archived in Zenodo under the DOI and is licensed under .