Learning to integrate web taxonomies with fine-grained relations: A case study using maximum entropy model

Chia Wei Wu, Tzong Han Tsai, Wen Lian Hsu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

As web taxonomy integration is an emerging issue on the Internet, many research topics, such as personalization, web searches, and electronic markets, would benefit from further development of taxonomy integration techniques. The integration task is to transfer documents from a source web taxonomy to a target web taxonomy. In most current techniques, integration performance is enhanced by referring to the relations between corresponding categories in the source and target taxonomies. However, the techniques may not be effective, since the concepts of the corresponding categories may overlap partially. In this paper we present an effective approach for integrating taxonomies and alleviating the partial overlap problem by considering fine-grained relations using a Maximum Entropy Model. The experiment results show that the proposed approach improves the classification accuracy of taxonomies over previous approaches.

Original languageEnglish
Title of host publicationInformation Retrieval Technology - Second Asia Information Retrieval Symposium, AIRS 2005, Proceedings
PublisherSpringer Verlag
Pages190-205
Number of pages16
ISBN (Print)3540291865, 9783540291862
DOIs
StatePublished - 2005
Event2nd Asia Information Retrieval Symposium, AIRS 2005 - Jeju Island, Korea, Republic of
Duration: 13 Oct 200515 Oct 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3689 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd Asia Information Retrieval Symposium, AIRS 2005
Country/TerritoryKorea, Republic of
CityJeju Island
Period13/10/0515/10/05

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