On the Complexity of Learning Description Logic Ontologies

Abstract

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.

Presented at the Reasoning Web Summer School 2020

References in corpus (2)

• Learning Deterministic Weighted Automata with Queries and Counterexamples
• Learning Ontologies with Epistemic Reasoning: The EL Case