mlsanityData to 7 Oct 2026

Ontolearn-A Framework for Large-scale OWL Class Expression Learning in Python

arXiv 2510.11561 ↗First posted 13 Oct 2025cs.LG cs.SCcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 7 of its 24 references that are in the map. First posted on arXiv 13 Oct 2025.

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7 of 24 references.

References · 7

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  2. 2020Knowledge Graphscited by 619
  3. 2018Efficient Concept Induction for Description Logicscited by 17
  4. 2021EvoLearner: Learning Description Logics with Evolutionary Algorithmscited by 17
  5. 2021Learning Concept Lengths Accelerates Concept Learning in ALCcited by 10
  6. 2021Neural Class Expression Synthesiscited by 6
  7. 2023Learning Permutation-Invariant Embeddings for Description Logic Conceptscited by 1

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Citing papers per year: 2025: 1; 2026: 1.

2 papers.