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dc.contributor.authorChen, Hsinchun
dc.contributor.authorFan, Haiyan
dc.contributor.authorChau, Michael
dc.contributor.authorZeng, Daniel
dc.date.accessioned2004-08-16T00:00:01Z
dc.date.available2010-06-18T23:23:50Z
dc.date.issued2001en_US
dc.date.submitted2004-08-16en_US
dc.identifier.citationMetaSpider: Meta-Searching and Categorization on the Web 2001, 52(13):1134-1147 Journal of the American Society for Information Science & Technologyen_US
dc.identifier.urihttp://hdl.handle.net/10150/105331
dc.descriptionArtificial Intelligence Lab, Department of MIS, Univeristy of Arizonaen_US
dc.description.abstractIt has become increasingly difficult to locate relevant information on the Web, even with the help of Web search engines. Two approaches to addressing the low precision and poor presentation of search results of current search tools are studied: meta-search and document categorization. Meta-search engines improve precision by selecting and integrating search results fromgeneric or domain-specific Web search engines or other resources. Document categorization promises better organization and presentation of retrieved results. This article introduces MetaSpider, a meta-search engine that has real-time indexing and categorizing functions. We report in this paper the major components of MetaSpider and discuss related technical approaches. Initial results of a user evaluation study comparing Meta- Spider, NorthernLight, and MetaCrawler in terms of clustering performance and of time and effort expended show that MetaSpider performed best in precision rate, but disclose no statistically significant differences in recall rate and time requirements. Our experimental study also reveals that MetaSpider exhibited a higher level of automation than the other two systems and facilitated efficient searching by providing the user with an organized, comprehensive view of the retrieved documents.
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherWiley Periodicals, Incen_US
dc.subjectWeb Miningen_US
dc.subjectInterneten_US
dc.subjectKnowledge Managementen_US
dc.subjectWorld Wide Weben_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial Intelligence laben_US
dc.subject.otherAI laben_US
dc.subject.otherMetaSpideren_US
dc.titleMetaSpider: Meta-Searching and Categorization on the Weben_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalJournal of the American Society for Information Science & Technologyen_US
refterms.dateFOA2018-08-21T11:28:52Z
html.description.abstractIt has become increasingly difficult to locate relevant information on the Web, even with the help of Web search engines. Two approaches to addressing the low precision and poor presentation of search results of current search tools are studied: meta-search and document categorization. Meta-search engines improve precision by selecting and integrating search results fromgeneric or domain-specific Web search engines or other resources. Document categorization promises better organization and presentation of retrieved results. This article introduces MetaSpider, a meta-search engine that has real-time indexing and categorizing functions. We report in this paper the major components of MetaSpider and discuss related technical approaches. Initial results of a user evaluation study comparing Meta- Spider, NorthernLight, and MetaCrawler in terms of clustering performance and of time and effort expended show that MetaSpider performed best in precision rate, but disclose no statistically significant differences in recall rate and time requirements. Our experimental study also reveals that MetaSpider exhibited a higher level of automation than the other two systems and facilitated efficient searching by providing the user with an organized, comprehensive view of the retrieved documents.


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