Similarity Metrics for Set of Experience Knowledge Structure
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.
KeywordsSimilarity Measure Knowledge Management Data Object Similarity Metrics Decision Event
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- 1.Arnold, W., Bowie, J.: Artificial Intelligence: A Personal Commonsense Journey, p. 46. Prentice Hall, Inc., Englewood Cliffs (1985)Google Scholar
- 2.Drucker, P.: The Post-Capitalist Executive: Managing in a Time of Great Change. Penguin, New York (1995)Google Scholar
- 5.Goldratt, E.M., Cox, J.: The Goal, Grover, Aldershot, Hants (1986)Google Scholar
- 6.Lin, B., Lin, C., et al.: A Knowledge Management Architecture in Collaborative Supply Chain. Journal of Computer Information Systems 42(5), 83–94 (2002)Google Scholar
- 7.Lloyd, J.W.: Logic for Learning: Learning Comprehensible Theories from Structure Data. Springer, Berlin (2003)Google Scholar
- 8.Malhotra, Y.: From Information Management to Knowledge Management: Beyond the ’Hi-Tech Hidebound’ Systems. In: Srikantaiah, K., Koening, M.E.D. (eds.) Knowledge Management for the Information Professional, Information Today, Inc., pp. 37–61 (2000)Google Scholar
- 9.Moen, P.: Attribute, Event Sequence, and Event Type Similarity Notions for Data Mining. Department of Computer Science Series of Publications A Report A-2000-1, Helsinki University Printing House, Helsinki (2000)Google Scholar
- 10.Noble, D.: Distributed Situation Assessment. In: Proceedings: FUSION 1998 International Conference (1998)Google Scholar
- 11.Sanin, C., Szczerbicki, E.: Knowledge Supply Chain System: A Conceptual Model. In: Szuwarzynski, A. (ed.) Knowledge Management: Selected Issues, pp. 79–97. Gdansk University Press, Gdansk (2004)Google Scholar
- 12.Sanin, C., Szczerbicki, E.: Set of Experience: A Knowledge Structure for Formal Decision Events. Foundations of Control and Management Sciences 3, 95–113 (2005)Google Scholar
- 14.White, D.A., Jain, R.: Algorithms and strategies for similarity retrieval. Technical Report VCL-96-101, Visual Computing Laboratory, University of California, San Diego, CA, USA (July 1996)Google Scholar