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Failure Analysis for Domain Knowledge Acquisition in a Knowledge-Intensive CBR System

  • Amélie Cordier
  • Béatrice Fuchs
  • Jean Lieber
  • Alain Mille
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4626)

Abstract

A knowledge-intensive case-based reasoning system has profit of the domain knowledge, together with the case base. Therefore, acquiring new pieces of domain knowledge should improve the accuracy of such a system. This paper presents an approach for knowledge acquisition based on some failures of the system. The cbr system is assumed to produce solutions that are consistent with the domain knowledge but that may be inconsistent with the expert knowledge, and this inconsistency constitutes a failure. Thanks to an interactive analysis of this failure, some knowledge is acquired that contributes to fill the gap from the system knowledge to the expert knowledge. Another type of failures occurs when the solution produced by the system is only partial: some additional pieces of information are required to use it. Once again, an interaction with the expert involves the acquisition of new knowledge. This approach has been implemented in a prototype, called FrakaS, and tested in the application domain of breast cancer treatment decision support.

Keywords

Domain Knowledge Knowledge Acquisition Failure Analysis Description Logic Knowledge Engineer 
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 2007

Authors and Affiliations

  • Amélie Cordier
    • 1
  • Béatrice Fuchs
    • 1
  • Jean Lieber
    • 2
  • Alain Mille
    • 1
  1. 1.LIRIS CNRS, UMR 5202, Université Lyon 1, INSA Lyon, Université Lyon 2, ECL, 43, bd du 11 Novembre 1918, Villeurbanne CedexFrance
  2. 2.Orpailleur team, LORIA UMR 7503 CNRS, INRIA, Nancy Universities, BP 239 54 506 Vandœuvre-lès-NancyFrance

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