A Method of Similarity-Driven Knowledge Revision for Type Specializations

Authors: Nobuhiro Morita, Makoto Haraguchi, and Yoshiaki Okubo.

Source: Lecture Notes in Artificial Intelligence Vol. 1720, 1999, 194 - 205.

This paper proposes a new framework of knowledge revision, called Similarity-Driven Knowledge Revision. Our revision is invoked based on a similarity observation by users and is intended to match with the observation. Particularly, we are concerned with a revision strategy according to which an inadequate variable typing in describing an object-oriented knowledge base is revised by specializing the typing to more specific one without loss of the original inference power. To realize it, we introduce a notion of extended sorts that can be viewed as a concept not appearing explicitly in the original knowledge base. If a variable typing with some sort is considered over-general, the typing is modified by replacing it with more specific extended sort. Such an extended sort can efficiently be identified by forward reasoning with SOL-deduction from the original knowledge base. Some experimental results show the use of SOL-deduction can drastically improve the computational efficiency.

©Copyright 1999 Springer-Verlag