Resolving Inconsistencies in Probabilistic Knowledge Bases

  • Marc Finthammer
  • Gabriele Kern-Isberner
  • Manuela Ritterskamp
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4667)

Abstract

The focus of this paper is on the practical aspects of efficiently resolving inconsistencies when merging probabilistic rule sets. We consider the problem of prioritized merging, when one core knowledge base is to be used without modifications and to be extended by information from other sources. This problem is addressed by our flexible system Heureka that aims at restoring consistency by finding those parts of the additional rule bases which are compatible with the core base and are considered most valuable by the user. We give an overview on the methodological framework of the system and describe some details of its main techniques. In particular, Heureka offers a convenient interface to inductive probabilistic reasoning on maximum entropy. An example from the domain of auditing illustrates the problem and the practical applicability of our framework.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Marc Finthammer
    • 1
  • Gabriele Kern-Isberner
    • 1
  • Manuela Ritterskamp
    • 1
  1. 1.Department of Computer Science, University of DortmundGermany

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