On the Complexity of Learning Description Logic Ontologies

TL;DR

This paper examines the computational complexity of learning description logic ontologies, providing formal models and reviewing existing complexity results and approaches in the field of ontology learning.

Contribution

It offers a formal specification of learning models for DL ontologies and synthesizes known complexity results and alternative methods from the literature.

Findings

Complexity results for learning lightweight DL ontologies

Formal models for ontology learning in computational learning theory

Overview of various approaches for DL ontology learning

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.