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Comparing Inconsistency Resolutions in Multi-Context Systems

  • Antonius Weinzierl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7415)

Abstract

Inconsistency in heterogeneous knowledge-integration systems with non-monotonic information exchange is a major concern as it renders systems useless at its occurrence. For the knowledge-integration framework of Multi-Context Systems, the problem of finding all possible resolutions to inconsistency has been addressed previously and some basic steps have been proposed to find most preferred resolutions. Here, we refine the techniques of finding preferred resolutions of inconsistency in two directions. First, we extend available qualitative methods using domain knowledge on the intention and category of information exchange to minimize the number of categories that are affected by a resolution. Second, we present a quantitative inconsistency measure for inconsistency resolutions, being suitable for scenarios where no further domain knowledge is available.

Keywords

Logic Program Human Insulin Preference Order Belief State Prefer Resolution 
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

  • Antonius Weinzierl
    • 1
  1. 1.Knowledge-Based Systems GroupVienna University of TechnologyAustria

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