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Algorithms for Paraconsistent Reasoning with OWL

  • Yue Ma
  • Pascal Hitzler
  • Zuoquan Lin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4519)

Abstract

In an open, constantly changing and collaborative environment like the forthcoming Semantic Web, it is reasonable to expect that knowledge sources will contain noise and inaccuracies. Practical reasoning techniques for ontologies therefore will have to be tolerant to this kind of data, including the ability to handle inconsistencies in a meaningful way. For this purpose, we employ paraconsistent reasoning based on four-valued logic, which is a classical method for dealing with inconsistencies in knowledge bases. Its transfer to OWL DL, however, necessitates the making of fundamental design choices in dealing with class inclusion, which has resulted in differing proposals for paraconsistent description logics in the literature. In this paper, we build on one of the more general approaches which due to its flexibility appears to be most promising for further investigations. We present two algorithms suitable for implementation, one based on a preprocessing before invoking a classical OWL reasoner, the other based on a modification of the KAON2 transformation algorithms. We also report on our implementation, called ParOWL.

Keywords

Description Logic Contradictory Information Atomic Concept Material Inclusion Class Inclusion 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Yue Ma
    • 1
    • 2
  • Pascal Hitzler
    • 2
  • Zuoquan Lin
    • 1
  1. 1.Department of Information Science, Peking UniversityChina
  2. 2.AIFB, Universität KarlsruheGermany

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