文章基本信息

标题：A Rule Learning Mechanism for Integration of Concept Hierarchies
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作者：Ryutaro ICHISE ; Masahiro HAMASAKI ; Hideaki TAKEDA 等
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2004
卷号：19
期号：6
页码：521-529
DOI：10.1527/tjsai.19.521
出版社：The Japanese Society for Artificial Intelligence
摘要：With the rapid advance of information technology, we are able to easily and quickly obtain a great deal of information on almost any topic. One method by which to managing such large amounts of information is to utilize catalogs which organize information within concept hierarchies. However, the concept hierarchy for each catalog is different because one concept hierarchy is not sufficient for all purposes. In the present paper, we address the problem of integrating multiple catalogs for ease of use. The primary problem lies in finding a suitable category in a catalog for each information instance in another catalog. Three approaches can be used to solve this problem: ontology integration approach, instance classification approach and category alignment approach based on categorization similarity. The main idea of this paper is a multiple strategy approach to combine the instance classification approach and the category alignment approach. In order to evaluate the proposed method, we conducted experiments using two actual Internet directories, Yahoo! and Google. The obtained results show that the proposed method improves upon or is competitive with the integration method based only on category alignment or instance classification. Therefore, the proposed catalog integration method is shown to be an effective combination of the instance classification approach and the category alignment approach.
关键词：machine learning ; data integration ; catalog ; concept hierarchy ; web