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Concept-Based Semantic Difference in Expressive Description Logics

  • Rafael S. Gonçalves
  • Bijan Parsia
  • Ulrike Sattler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7649)

Abstract

Detecting, much less understanding, the difference between two description logic based ontologies is challenging for ontology engineers due, in part, to the possibility of complex, non-local logic effects of axiom changes. First, it is often quite difficult to even determine which concepts have had their meaning altered by a change. Second, once a concept change is pinpointed, the problem of distinguishing whether the concept is directly or indirectly affected by a change has yet to be tackled. To address the first issue, various principled notions of “semantic diff” (based on deductive inseparability) have been proposed in the literature and shown to be computationally practical for the expressively restricted case of \({\mathcal ELH}^r\)-terminologies. However, problems arise even for such limited logics as \({\mathcal ALC}\): First, computation gets more difficult, becoming undecidable for logics such as \({\mathcal SROIQ}\) which underly the Web Ontology Language (OWL). Second, the presence of negation and disjunction make the standard semantic difference too sensitive to change: essentially, any logically effectual change always affects all terms in the ontology. In order to tackle these issues, we formulate the central notion of finding the minimal change set based on model inseparability, and present a method to differentiate changes which are specific to (thus directly affect) particular concept names. Subsequently we devise a series of computable approximations, and compare the variously approximated change sets over a series of versions of the NCI Thesaurus (NCIt).

Keywords

Description Logic Conservative Extension Concept Hierarchy Expressive Logic Indirect Change 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Rafael S. Gonçalves
    • 1
  • Bijan Parsia
    • 1
  • Ulrike Sattler
    • 1
  1. 1.School of Computer ScienceUniversity of ManchesterManchesterUnited Kingdom

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