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    Discovering semantic aspects of socially constructed knowledge hierarchy to boost the relevance of Web searching

    129675_12688_2009_Journal_Apr2009_JUCS.pdf (681.9Kb)
    Access Status
    Open access
    Authors
    Zhu, Dengya
    Dreher, Heinz
    Date
    2009
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Zhu, Dengya and Dreher, Heinz. 2009. Discovering semantic aspects of socially constructed knowledge hierarchy to boost the relevance of Web searching. Journal of Universal Computer Science. 15 (8): pp. 1685-1710.
    Source Title
    Journal of Universal Computer Science J.UCS
    Additional URLs
    http://www.jucs.org/jucs_15_8
    ISSN
    09486968
    Faculty
    Curtin Business School
    School of Information Systems
    Remarks

    Copyright © 2009 J.UCS Journal of Universal Computer Science

    URI
    http://hdl.handle.net/20.500.11937/22984
    Collection
    • Curtin Research Publications
    Abstract

    The research intends to boost the relevance of Web search results by classifyingWebsnippet into socially constructed hierarchical search concepts, such as the mostcomprehensive human edited knowledge structure, the Open Directory Project (ODP). Thesemantic aspects of the search concepts (categories) in the socially constructed hierarchicalknowledge repositories are extracted from the associated textual information contributed bysocieties. The textual information is explored and analyzed to construct a category-documentset, which is subsequently employed to represent the semantics of the socially constructedsearch concepts. Simple API for XML (SAX), a component of JAXP (Java API for XMLProcessing) is utilized to read in and analyze the two RDF format ODP data files, structure.rdfand content.rdf. kNN, which is trained by the constructed category-document set, is used tocategorized the Web search results. The categorized Web search results are then ontologicallyfiltered based on the interactions of Web information seekers. Initial experimental resultsdemonstrate that the proposed approach can improve precision by 23.5%.

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