Soundness preserving approximation for TBox reasoning

Y. Ren, J.Z. Pan, Y. Zhao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

27 Citations (Scopus)

Abstract

Large scale ontology applications require efficient and robust description logic (DL) reasoning services. Expressive DLs usually have very 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. In this paper, we present a soundness preserving approximate reasoning framework for TBox reasoning in OWL2-DL. The ontologies are encoded into eL with additional data structures. A tractable algorithm is presented to classify such approximation by realizing more and more inference patterns. Preliminary evaluation shows that our approach can classify existing benchmarks in large scale efficiently with a high recall.
Original languageEnglish
Title of host publicationProceedings of the Twenty-fourth AAAI Conference on Artificial Intelligence and the Twenty-second Innovative Applications of Artificial Intelligence Conference, 11-15 July, 2010, Atlanta, Georgia, USA
Place of PublicationMenlo Park, California
PublisherAAAI Press
Pages351-356
Number of pages6
ISBN (Print)9781577354635
Publication statusPublished - 1 Jan 2010
Event24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10 / IAAI-10 - Atlanta, United States
Duration: 10 Jul 201015 Jul 2010

Conference

Conference24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10 / IAAI-10
CountryUnited States
CityAtlanta
Period10/07/1015/07/10

Keywords

  • approximate reasoning
  • description logic
  • expressive power
  • inference

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