Discovery of concept entities from web sites using web unit mining

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 ai...

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Main Authors: Yin, Ming, Goh, Dion Hoe-Lian, Lim, Ee Peng, Sun, Aixin
Other Authors: Wee Kim Wee School of Communication and Information
Format: Article
Language:English
Published: 2010
Subjects:
Online Access:https://hdl.handle.net/10356/93769
http://hdl.handle.net/10220/6193
http://www.emeraldinsight.com/Insight/viewContentItem.do?contentType=Article&hdAction=lnkpdf&contentId=1614095
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Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-93769
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spelling sg-ntu-dr.10356-937692019-12-06T18:45:13Z Discovery of concept entities from web sites using web unit mining Yin, Ming Goh, Dion Hoe-Lian Lim, Ee Peng Sun, Aixin Wee Kim Wee School of Communication and Information DRNTU::Engineering::Computer science and engineering::Computer systems organization::Computer-communication networks 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. Published version 2010-02-19T06:32:34Z 2019-12-06T18:45:13Z 2010-02-19T06:32:34Z 2019-12-06T18:45:13Z 2005 2005 Journal Article Yin, M., Goh, H. L., Lim, E P., & Sun, A. (2005). Discovery of concept entities from web sites using web unit mining. International Journal of Web Information Systems, 20, 1-13. 1744-0084 https://hdl.handle.net/10356/93769 http://hdl.handle.net/10220/6193 http://www.emeraldinsight.com/Insight/viewContentItem.do?contentType=Article&hdAction=lnkpdf&contentId=1614095 en International journal of web information systems 13 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering::Computer systems organization::Computer-communication networks
spellingShingle DRNTU::Engineering::Computer science and engineering::Computer systems organization::Computer-communication networks
Yin, Ming
Goh, Dion Hoe-Lian
Lim, Ee Peng
Sun, Aixin
Discovery of concept entities from web sites using web unit mining
description 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.
author2 Wee Kim Wee School of Communication and Information
author_facet Wee Kim Wee School of Communication and Information
Yin, Ming
Goh, Dion Hoe-Lian
Lim, Ee Peng
Sun, Aixin
format Article
author Yin, Ming
Goh, Dion Hoe-Lian
Lim, Ee Peng
Sun, Aixin
author_sort Yin, Ming
title Discovery of concept entities from web sites using web unit mining
title_short Discovery of concept entities from web sites using web unit mining
title_full Discovery of concept entities from web sites using web unit mining
title_fullStr Discovery of concept entities from web sites using web unit mining
title_full_unstemmed Discovery of concept entities from web sites using web unit mining
title_sort discovery of concept entities from web sites using web unit mining
publishDate 2010
url https://hdl.handle.net/10356/93769
http://hdl.handle.net/10220/6193
http://www.emeraldinsight.com/Insight/viewContentItem.do?contentType=Article&hdAction=lnkpdf&contentId=1614095
_version_ 1681039995916255232