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A Knowledge Revision Method based on Similarity Observations

一种基于相似性观测的知识修正方法

基本信息

批准号：
11680375
负责人：
HARAGUCHI Makoto
金额：
$ 1.79万
依托单位：
Hokkaido University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1999
资助国家：
日本
起止时间：
1999 至 2000
项目状态：
已结题

项目摘要

We proposes a new framework of knowledge revision, called Similarity-Driven Knowledge Revision.In our framework, the 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 typing in describing an object-oriented knowledge base is corrected and revised by specializing the inadequate types to more specific ones without loss of original inference power.That is, a minimal revision can be achieved according to our revision framework.In order to realize it, we introduce a notion of{\em extended sorts}.An extended sort can be viewed as a concept that does not appear explicitly in the original knowledge base.If a variable typing with some sort is considered too general in the original knowledge base, the typing is modified by replacing the general sort with more specific extended sort.Such an extended sort can be identified by forward reasoning from the original knowledge base.Especially, a forward reasoning with SOL-deduction is adopted to obtain extended sorts efficiently.Some experimental results show that the use of SOL-deduction can drastically improve the computational efficiency.

提出了一种新的知识修改框架，称为相似性驱动的知识修改框架。在该框架中，知识修改是基于用户的相似性观察来调用的，旨在与观察结果相匹配。特别是，我们提出了一种修改策略，根据该策略，在不损失原有推理能力的情况下，通过将不充分的类型专门化为更具体的类型来纠正和修改面向对象知识库中的不足类型。也就是说，根据该框架可以实现最小的修改。为了实现该策略，我们提出了一种新的知识修改策略，即在不损失原有推理能力的情况下，通过将不充分的类型专门化为更具体的类型来纠正和修改面向对象的知识库。为了实现该策略，我们提出了一种新的知识修改框架，称为相似度驱动的知识修改框架。该框架基于用户的相似性观察来调用知识修改，旨在与该观察相匹配。引入了扩展排序的概念。扩展排序可以看作是一个在原始知识库中没有显式出现的概念。如果在原始知识库中，带有某种排序的变量类型被认为过于通用，则通过用更具体的扩展排序来替换普通排序来修改类型。这样的扩展排序可以从原始知识库中通过正向推理来识别。特别是，采用具有SOL演绎的正向推理来高效地获得扩展排序。实验结果表明，SOL-演绎的使用可以显著提高计算效率。