Comprehensibility Improvement of Tabular Knowledge Bases

Atsushi Sugiura, Yoshiyuki Koseki, Maximilian Riesenhuber

This paper discusses the important. issue of knowledge base comprehensibility and describes a technique for comprehensibility improvement. Comprehensibility is often measured by simplicity of concept description. Even in the simplest form, however, there will be a number of different DNF (Disjunctive Normal Form) descriptions possible to represent the same concept, and each of these will have a different degree of comprehensibility. In other words, simplification does not necessarily guarantee improved comprehensibility. In this paper, the authors introduce three new comprehensibility criteria, similarity, continuity, and conformity, for use with tabular knowledge bases. In addition, they propose an algorithm to convert a decision table with poor comprehensibility to one with high comprehensibility, while preserving logical equivalency. In experiments, the algorithm generated either the same or similar tables to those generated by humans.

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