Tractable approximate deduction for OWL

Jeff Z. Pan, Yuan Ren, Yuting Zhao

Research output: Contribution to journalArticle

11 Citations (Scopus)
4 Downloads (Pure)

Abstract

Today's ontology applications require efficient and reliable description logic (DL) reasoning services. Expressive DLs usually have high worst case complexity while tractable DLs are restricted in terms of expressive power. This brings a new challenge: can users use expressive DLs to build their ontologies and still enjoy the efficient services as in tractable languages? Approximation has been considered as a solution to this challenge; however, traditional approximation approaches have limitations in terms of performance and usability. In this paper, we present a tractable approximate reasoning framework for OWL 2 that improves efficiency and guarantees soundness. Evaluation on ontologies from benchmarks and real-world use cases shows that our approach can do reasoning on complex ontologies efficiently with a high recall.
Original languageEnglish
Pages (from-to)95-155
Number of pages61
JournalArtificial Intelligence
Volume235
Early online date18 Jan 2016
DOIs
Publication statusPublished - Jun 2016

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deduction
ontology
Ontology
logic
guarantee
efficiency
Deduction
language
evaluation
performance
Expressive
Approximation

Keywords

  • Ontology
  • Approximation
  • OWL 2
  • Reasoning

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Tractable approximate deduction for OWL. / Pan, Jeff Z.; Ren, Yuan; Zhao, Yuting.

In: Artificial Intelligence, Vol. 235, 06.2016, p. 95-155.

Research output: Contribution to journalArticle

Pan, Jeff Z. ; Ren, Yuan ; Zhao, Yuting. / Tractable approximate deduction for OWL. In: Artificial Intelligence. 2016 ; Vol. 235. pp. 95-155.
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