JOURNAL ARTICLE

Exact Learning of Lightweight Description Logic Ontologies

Boris KonevCarsten LutzAna OzakiFrank Wolter

Year: 2017 Journal:   arXiv (Cornell University) Vol: 18 (201)Pages: 1-63   Publisher: Cornell University

Abstract

We study the problem of learning description logic (DL) ontologies in Angluin et al.'s framework of exact learning via queries. We admit membership queries ("is a given subsumption entailed by the target ontology?") and equivalence queries ("is a given ontology equivalent to the target ontology?"). We present three main results: (1) ontologies formulated in (two relevant versions of) the description logic DL-Lite can be learned with polynomially many queries of polynomial size; (2) this is not the case for ontologies formulated in the description logic EL, even when only acyclic ontologies are admitted; and (3) ontologies formulated in a fragment of EL related to the web ontology language OWL 2 RL can be learned in polynomial time. We also show that neither membership nor equivalence queries alone are sufficient in cases (1) and (3).

Keywords:
Description logic Learnability Equivalence (formal languages) Computer science Logical consequence Polynomial Signature (topology) Theoretical computer science Artificial intelligence Algebra over a field Discrete mathematics Mathematics Pure mathematics Geometry

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Citation History

Topics

Natural Language Processing Techniques
Physical Sciences →  Computer Science →  Artificial Intelligence
Semantic Web and Ontologies
Physical Sciences →  Computer Science →  Artificial Intelligence
Topic Modeling
Physical Sciences →  Computer Science →  Artificial Intelligence

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