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.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Arnold, W., Bowie, J.: Artificial Intelligence: A Personal Commonsense Journey, p. 46. Prentice Hall, Inc., Englewood Cliffs (1985)
Drucker, P.: The Post-Capitalist Executive: Managing in a Time of Great Change. Penguin, New York (1995)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2001)
Fabrikant, S.I., Buttenfield, B.P.: Formalizing Semantic Spaces for Information Access. Annals of the Association of American Geographers 91(2), 263–280 (2001)
Goldratt, E.M., Cox, J.: The Goal, Grover, Aldershot, Hants (1986)
Lin, B., Lin, C., et al.: A Knowledge Management Architecture in Collaborative Supply Chain. Journal of Computer Information Systems 42(5), 83–94 (2002)
Lloyd, J.W.: Logic for Learning: Learning Comprehensible Theories from Structure Data. Springer, Berlin (2003)
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)
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)
Noble, D.: Distributed Situation Assessment. In: Proceedings: FUSION 1998 International Conference (1998)
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)
Sanin, C., Szczerbicki, E.: Set of Experience: A Knowledge Structure for Formal Decision Events. Foundations of Control and Management Sciences 3, 95–113 (2005)
Sanin, C., Szczerbicki, E.: Using XML for Implementing Set of Experience Knowledge Structure. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3681, pp. 946–952. Springer, Heidelberg (2005)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sanin, C., Szczerbicki, E. (2006). Similarity Metrics for Set of Experience Knowledge Structure. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892960_80
Download citation
DOI: https://doi.org/10.1007/11892960_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46535-5
Online ISBN: 978-3-540-46536-2
eBook Packages: Computer ScienceComputer Science (R0)