Similarity Metrics for Set of Experience Knowledge Structure

  • Cesar Sanin
  • Edward Szczerbicki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4251)


When referring to knowledge forms, collecting formal decision events in a knowledge-explicit way becomes an important development. Set of experience knowledge structure can assist in accomplishing this purpose. However, to make set of experience knowledge structure useful, it must be classifiable and comparable. The purpose of this paper is to show similarity metrics for set of experience knowledge structure, and within, similarity metrics for its components: variables, functions, constraints, and rules. A comparable and classifiable set of experience would make explicit knowledge of formal decision events useful elements in multiple systems and technologies.


Similarity Measure Knowledge Management Data Object Similarity Metrics Decision Event 
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 2006

Authors and Affiliations

  • Cesar Sanin
    • 1
  • Edward Szczerbicki
    • 1
  1. 1.Faculty of Engineering and Built EnvironmentUniversity of NewcastleCallaghanAustralia

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