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)


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.


Description Logic Stable Model Semantic Ground Clause Rule Head Inclusion Axiom 
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 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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