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    IR issues for digital ecosystems users

    20614_downloaded_stream_70.pdf (488.5Kb)
    Access Status
    Open access
    Authors
    Zhu, Dengya
    Dreher, Heinz
    Date
    2008
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Zhu, Dengya and Dreher, Heinz. 2008. : IR issues for digital ecosystems users, in Chang, Elizabeth and Hussain, Farookh Khadeer (ed), Second IEEE international conference on digital ecosystems and technologies (IEEE DEST 2008), 26 Feb 2008, pp. 586-591. Phitsanulok, Thailand: IEEE.
    Source Title
    Second IEEE international conference on digital ecosystems and technologies (IEEE DEST 2008)
    Source Conference
    Second IEEE international conference on digital ecosystems and technologies (IEEE DEST 2008)
    DOI
    10.1109/DEST.2008.4635203
    Faculty
    Curtin Business School
    School of Information Systems
    Remarks

    Copyright 2008 IEEE

    This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

    URI
    http://hdl.handle.net/20.500.11937/18797
    Collection
    • Curtin Research Publications
    Abstract

    The purpose of this research is to discuss some challenges of information retrieval, especially Web information retrieval, in digital ecosystems from a user?s perspective. As a dominant search tool, search engines usually return millions of search results in a long flat list in which many or even most of the results can be irrelevant. The long flat list conveys nothing about knowledge structure related to the retrieved results and personal search preferences and interests are not explored.Although some search engines try to cluster the Web results, the automatically formed titles and knowledge hierarchy is prone to mismatching the searcher?s human mental model. In digital ecosystems, while many different search tools are available, they are not integrated. To address these issues, a search framework which combines categorization, clustering, ontology, and personalization is proposed, and thus the quality of search results in digital ecosystems is expected to be boosted.

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