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Article: Using content-based and link-based analysis in building vertical search engines

TitleUsing content-based and link-based analysis in building vertical search engines
Authors
Issue Date2004
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2004, v. 3334, p. 515-518 How to Cite?
AbstractThis paper reports our research in the Web page filtering process in specialized search engine development. We propose a machine-learning-based approach that combines Web content analysis and Web structure analysis. Instead of a bag of words, each Web page is represented by a set of content-based and link-based features, which can be used as the input for various machine learning algorithms. The proposed approach was implemented using both a feedforward/backpropagation neural network and a support vector machine. An evaluation study was conducted and showed that the proposed approaches performed better than the benchmark approaches. © Springer-Verlag Berlin Heidelberg 2004.
Persistent Identifierhttp://hdl.handle.net/10722/177991
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorChau, Men_US
dc.contributor.authorChen, Hen_US
dc.date.accessioned2012-12-19T09:41:11Z-
dc.date.available2012-12-19T09:41:11Z-
dc.date.issued2004en_US
dc.identifier.citationLecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2004, v. 3334, p. 515-518en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/10722/177991-
dc.description.abstractThis paper reports our research in the Web page filtering process in specialized search engine development. We propose a machine-learning-based approach that combines Web content analysis and Web structure analysis. Instead of a bag of words, each Web page is represented by a set of content-based and link-based features, which can be used as the input for various machine learning algorithms. The proposed approach was implemented using both a feedforward/backpropagation neural network and a support vector machine. An evaluation study was conducted and showed that the proposed approaches performed better than the benchmark approaches. © Springer-Verlag Berlin Heidelberg 2004.en_US
dc.languageengen_US
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_US
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.titleUsing content-based and link-based analysis in building vertical search enginesen_US
dc.typeArticleen_US
dc.identifier.emailChau, M: mchau@hkucc.hku.hken_US
dc.identifier.authorityChau, M=rp01051en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-35048817047en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-35048817047&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume3334en_US
dc.identifier.spage515en_US
dc.identifier.epage518en_US
dc.publisher.placeGermanyen_US
dc.identifier.scopusauthoridChau, M=7006073763en_US
dc.identifier.scopusauthoridChen, H=8871373800en_US

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