DEVELOPING HETEROGENEOUS SIMILARITY METRICS FOR KNOWLEDGE ADMINISTRATION

Abstract

Collecting formal decision events in a knowledge-explicit way becomes an important development in terms of knowledge administration. A Set of Experience Knowledge Structure can assist in accomplishing this purpose. However, collecting knowledge comes together with mechanisms of classifying, comparing, and selecting elements among the collected universe, i.e., the universe of formal decision events. Thus, similarity metrics play an important role in knowledge administration. The purpose of this article is to develop heterogeneous similarity metrics for set of experience knowledge structure, and within it, 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 knowledge administration, as well as in multiple technologies.