Similarity-Driven Knowledge Revision for Intensional Errors

OKUBO Yoshiaki, MORITA Nobuhiro, HARAGUCHI Makoto

doi:10.1527/tjsai.18.1

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類似性の観察に基づく知識ベースの内包的エラー修正法
ルイジセイノカンサツニモトヅクチシキベースノナイホウテキエラーシュウセイホウ

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Abstract

In this paper, we propose a new framework of knowledge revision, Similarity-Driven Knowledge Revision. For an object-oriented knowledge base KB, our revision is triggered when a similarity between sort concepts detected from KB does not fit a user's intuition. We revise KB into a knowledge base from which such an undesirable similarity is not detected and in which the logical semantics of KB is still preserved. An observation of undesirable similarity is due to an over-general typing of variable in KB. In order to modify the typing, we introduce a notion of extended sorts that can be viewed as a sort 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 be efficiently identified by forward reasoning with SOL-resolution from the original knowledge base. Some experimental results show that the use of SOL-resolution can drastically improve the computational efficiency.

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Transactions of the Japanese Society for Artificial Intelligence

Transactions of the Japanese Society for Artificial Intelligence 18 (1), 1-14, 2003

The Japanese Society for Artificial Intelligence

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