Comprehensibility Improvement of Tabular Knowledge Bases

This paper discusses the important issue of knowledge base comprehensibility and describes a technique for comprehensibility improvement. Com-prehensibility is often measured by simplicity of concept description. Even in the simplest form, however, there will be a number of dierent DNF (Disjunctive Normal Form) descriptions possible to represent the same concept, and each of these will have a dierent degree of comprehensibility. In other words, simplication does not necessarily guarantee improved comprehensibility. In this paper , the authors introduce three new comprehen-sibility criteria, similarity, continuity, a n d 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 h umans.