Year: 2025
Type: preprint
Abstract: In this paper, we present Ontolearn-a framework for learning OWL class expressions over large knowledge graphs. Ontolearn contains efficient implementations of recent stateof-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.
Source: arXiv (Cornell University)
Authors: Demir, Caglar, Baci, Alkid, Kouagou, N'Dah Jean, Sieger, Leonie Nora, Heindorf, Stefan, Bin, Simon, Blübaum, Lukas, Bigerl, Alexander, Ngomo, Axel-Cyrille Ngonga
Institution: Paderborn University
Language: English
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7090847892
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