On the Complexity of Learning Description Logic Ontologies
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Abstract (arXiv)
Ontologies are a popular way of representing domain knowledge, in particular, knowledge in domains related to life sciences. (Semi-)automating the process of building an ontology has attracted researchers from different communities into a field called "Ontology Learning". We provide a formal specification of the exact and the probably approximately correct learning models from computational learning theory. Then, we recall from the literature complexity results for learning lightweight description logic (DL) ontologies in these models. Finally, we highlight other approaches proposed in the literature for learning DL ontologies.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 56 references that are in the map. First posted on arXiv 25 Mar 2021.
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7 of 56 references.
References · 7
- 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
- 2018The Tsetlin Machine -- A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logiccited by 71
- 2017Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamplescited by 66
- 2019Learning Deterministic Weighted Automata with Queries and Counterexamplescited by 22
- 2014The DL-Lite Family and Relationscited by 20
- 2017Exact Learning of Lightweight Description Logic Ontologiescited by 10
- 2019Learning Ontologies with Epistemic Reasoning: The EL Casecited by 1
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Citing papers per year: 2021: 1.
1 paper.