Ontolearn---a Framework for Large-Scale OWL Class Expression Learning in Python
Abstract
In this paper, we present Ontolearn---a framework for learning OWL class expressions over large knowledge graphs. Ontolearn contains efficient implementations of recent state-of-the-art symbolic and neuro-symbolic class expression learners including EvoLearner and DRILL. A learned OWL class expression can be used to classify instances in the knowledge graph. Furthermore, Ontolearn integrates a verbalization module based on an LLM to translate complex OWL class expressions into natural language sentences. By mapping OWL class expressions into respective SPARQL queries, Ontolearn can be easily used to operate over a remote triplestore. The source code of Ontolearn is available at https://github.com/dice-group/Ontolearn.
Cite
Text
Demir et al. "Ontolearn---a Framework for Large-Scale OWL Class Expression Learning in Python." Machine Learning Open Source Software, 2025.Markdown
[Demir et al. "Ontolearn---a Framework for Large-Scale OWL Class Expression Learning in Python." Machine Learning Open Source Software, 2025.](https://mlanthology.org/mloss/2025/demir2025jmlr-ontolearn/)BibTeX
@article{demir2025jmlr-ontolearn,
title = {{Ontolearn---a Framework for Large-Scale OWL Class Expression Learning in Python}},
author = {Demir, Caglar and Baci, Alkid and Kouagou, N'Dah Jean and Sieger, Leonie Nora and Heindorf, Stefan and Bin, Simon and Blübaum, Lukas and Bigerl, Alexander and Ngomo, Axel-Cyrille Ngonga},
journal = {Machine Learning Open Source Software},
year = {2025},
pages = {1-6},
volume = {26},
url = {https://mlanthology.org/mloss/2025/demir2025jmlr-ontolearn/}
}