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:

bash pip install ontolearner

Next, verify the installation: ```python import ontolearner

print(ontolearner.version) ```

Please refer to Installation page for further options.

🔗 Essential Resources

| Resource | Info | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 📚 OntoLearner Documentation | OntoLearner's extensive documentation website. | | 🤗 Datasets on Hugging Face | Access curated, machine-readable ontologies. | | Quick Tour on OntoLearner version=1.2.1 | OntoLearner hands-on Colab tutorials. | | 🚀 Quickstart | Get started quickly with OntoLearner’s main features and workflow. | | 🕸️ Learning Tasks | Explore supported ontology learning tasks like LLMs4OL Paradigm tasks and Text2Onto. | | | 🧠 Learner Models | Browse and configure various learner models, including LLMs, Retrieval, or RAG approaches. | | 📚 Ontologies Documentations | Review benchmark ontologies and datasets used for evaluation and training. | | 🧩 How to work with Ontologizer? | Learn how to modularize and preprocess ontologies using the Ontologizer module. |

🚀 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 - Automatic Download from Hugging Face: ```python from ontolearner import Wine

1. Initialize an ontologizer from OntoLearner

ontology = Wine()

2. Load the ontology automatically from HuggingFace

ontology.load()

3. Extract the learning task dataset

data = ontology.extract() ```

To see the ontology metadata you can print the ontology: python print(ontology)

Now, explore 150+ ready-to-use ontologies or read on how to work with ontologizers.

Learner Models:

```python from ontolearner import AutoRetrieverLearner, AgrO, traintestsplit, evaluation_report

1. Programmatic import of an ontology

ontology = AgrO() ontology.load()

2. Load tasks datasets

ontological_data = ontology.extract()

3. Split into train and test sets

traindata, testdata = traintestsplit(ontologicaldata, testsize=0.2, random_state=42)

4. Initialize Learner

task = 'non-taxonomic-re' retlearner = AutoRetrieverLearner(topk=5) retlearner.load(modelid='sentence-transformers/all-MiniLM-L6-v2')

5. Fit the model to training data and do the predict

retlearner.fit(traindata, task=task) predicts = retlearner.predict(testdata, task=task)

6. Evaluation

truth = retlearner.tasksgroundtruthformer(data=testdata, task=task) metrics = evaluationreport(ytrue=truth, ypred=predicts, task=task) print(metrics) ``` Other learners: * LLM-Based Learner * RAG-Based Learner

LearnerPipeline: The OntoLearner also offers a streamlined LearnerPipeline class that simplifies the entire process of initializing, training, predicting, and evaluating a RAG setup into a single call.

```python

Import core components from the OntoLearner library

from ontolearner import LearnerPipeline, AgrO, traintestsplit

Load the AgrO ontology, which includes structured agricultural knowledge

ontology = AgrO() ontology.load() # Load ontology data (e.g., entities, relations, metadata)

Extract relation instances from the ontology and split them into training and test sets

traindata, testdata = traintestsplit( ontology.extract(), # Extract annotated (head, tail, relation) triples testsize=0.2, # 20% for evaluation randomstate=42 # Ensures reproducible splits )

Initialize the learning pipeline using a dense retriever

pipeline = LearnerPipeline( retrieverid='sentence-transformers/all-MiniLM-L6-v2', # Hugging Face model ID for retrieval batchsize=10, # Number of samples to process per batch (if batching is enabled internally) top_k=5 # Retrieve top-5 most relevant support instance per query )

Run the pipeline on the training and test data

The pipeline performs: fit() → predict() → evaluate() in sequence

outputs = pipeline( traindata=traindata, testdata=testdata, evaluate=True, # If True, computes precision, recall, and F1-score task='non-taxonomic-re' # Specifies that we are doing non-taxonomic relation prediction )

Print the evaluation metrics (precision, recall, F1)

print("Metrics:", outputs['metrics'])

Print the total elapsed time for training and evaluation

print("Elapsed time:", outputs['elapsed_time'])

Print the full output dictionary (includes predictions)

print(outputs) ```

⭐ 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:

bibtex @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: bibtex @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 = {v1.3.0}, doi = {10.5281/zenodo.15399783}, url = {https://doi.org/10.5281/zenodo.15399783}, }

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