Similarity metrics for set of experience knowledge structure

Authors:
Cesar Sanin;Edward Szczerbicki
Affiliations:
Faculty of Engineering and Built Environment, University of Newcastle, Callaghan, NSW, Australia;Faculty of Engineering and Built Environment, University of Newcastle, Callaghan, NSW, Australia
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Year:
2006

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Abstract

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.