Soundness preserving approximation for TBox reasoning

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

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

Abstract / Description of output

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
PublisherAAAI Press
Number of pages6
ISBN (Electronic)978-1-57735-462-8
ISBN (Print)978-1-57735-463-5
Publication statusPublished - 15 Jul 2010
EventTwenty-Fourth AAAI Conference on Artificial Intelligence - Atlanta, United States
Duration: 11 Jul 201015 Nov 2010
Conference number: 24

Publication series

NameTwenty-Fourth AAAI Conference on Artificial Intelligence
PublisherAAAI Press
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468


ConferenceTwenty-Fourth AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI 2010
Country/TerritoryUnited States
Internet address

Keywords / Materials (for Non-textual outputs)

  • approximate reasoning
  • description logic
  • expressive power
  • inference


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