Paraconsistent Semantics for Hybrid MKNF Knowledge Bases

  • Shasha Huang
  • Qingguo Li
  • Pascal Hitzler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6902)

Abstract

Hybrid MKNF knowledge bases, originally based on the stable model semantics, is a mature method of combining rules and Description Logics (DLs). The well-founded semantics for such knowledge bases has been proposed subsequently for better efficiency of reasoning. However, integration of rules and DLs may give rise to inconsistencies, even if they are respectively consistent. Accordingly, reasoning systems based on the previous two semantics will break down. In this paper, we employ the four-valued logic proposed by Belnap, and present a paraconsistent semantics for Hybrid MKNF knowledge bases, which can detect inconsistencies and handle it effectively. Besides, we transform our proposed semantics to the stable model semantics via a linear transformation operator, which indicates that the data complexity in our paradigm is not higher than that of classical reasoning. Moreover, we provide a fixpoint algorithm for computing paraconsistent MKNF models.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Shasha Huang
    • 1
  • Qingguo Li
    • 1
  • Pascal Hitzler
    • 2
  1. 1.College of Mathematics and EconometricsHunan UniversityChina
  2. 2.Kno.e.sis CenterWright State UniversityDaytonUSA

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