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Journal cover: International Journal of Web Information Systems

International Journal of Web Information Systems

ISSN: 1744-0084

Online from: 2005

Subject Area: Information and Knowledge Management

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Discovery of concept entities from web sites using web unit mining


Document Information:
Title:Discovery of concept entities from web sites using web unit mining
Author(s):Ming Yin Ming, (Division of Information Studies, School of Communication & Information, Nanyang Technological University, Singapore 639798), Dion Hoe-lian Goh, (Division of Information Studies, School of Communication & Information, Nanyang Technological University, Singapore 639798), Ee-Peng Lim, (Centre for Advanced Information Systems, School of Computer Engineering, Nanyang Technological University, Singapore 639798), Aixin Sun, (School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia 2052)
Citation:Ming Yin Ming, Dion Hoe-lian Goh, Ee-Peng Lim, Aixin Sun, (2005) "Discovery of concept entities from web sites using web unit mining", International Journal of Web Information Systems, Vol. 1 Iss: 3, pp.123 - 136
Keywords:Web classification, Web information organization
Article type:Technical paper
DOI:10.1108/17440080580000088 (Permanent URL)
Publisher:Emerald Group Publishing Limited
Abstract:A web site usually contains a large number of concept entities, each consisting of one or more web pages connected by hyperlinks. In order to discover these concept entities for more expressive web site queries and other applications, the web unit mining problem has been proposed. Web unit mining aims to determine web pages that constitute a concept entity and classify concept entities into categories. Nevertheless, the performance of an existing web unit mining algorithm, iWUM, suffers as it may create more than one web unit (incomplete web units) from a single concept entity. This paper presents two methods to solve this problem. The first method introduces a more effective web fragment construction method so as reduce later classification errors. The second method incorporates site-specific knowledge to discover and handle incomplete web units. Experiments show that incomplete web units can be removed and overall accuracy has been significantly improved, especially on the precision and F1 measures.



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