Conceptual Clustering in Knowledge Organization

Authors:
Yizong Cheng;King-Sun Fu
Affiliations:
School of Electrical Engineering, Purdue University, West Lafayette, IN 47907.;School of Electrical Engineering, Purdue University, West Lafayette, IN.
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1985

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Abstract

Knowledge organization is a very important step in building an expert system. The problem is how to organize knowledge into a conceptual structure and thus make it complete, concise, and consistent. In this paper, concepts used in knowledge description are divided into tangible ones and intermediate ones depending on whether or not they appear in the input or the output of the system. Intermediate concepts and their relationships with tangible concepts are subjected to changes. A distance measure for rules and an algorithm for conceptual clustering are described. New intermediate concepts are generated using this algorithm. A few new concepts may replace a large number of old relationships and also generate new rules for the system. An experiment on traditional Chinese medicine shows that the proposed method produces results similar to those generated by experts.