Using machine learning to support resource quality assessment: An adaptive attribute-based approach for health information portals

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    14 Citations (Scopus)


    Labor-intensity of resource quality assessment is a bottleneck for content management in metadata-driven health information portals. This research proposes an adaptive attribute-based approach to assist informed judgments when assessing the quality of online information resources. It employs intelligent learning techniques to predict values of resource quality attributes based on previous value judgments encoded in resource metadata descriptions. The proposed approach is implemented as an intelligent quality attribute learning component of a portal’s content management system. This paper introduces the required machine learning procedures for the implementation of the component. Its prediction performance was evaluated via a series of machine learning experiments, which demonstrated the feasibility and the potential usefulness of the proposed approach.

    Original languageEnglish
    Title of host publicationDatabase Systems for Adanced Applications
    Subtitle of host publication16th International Conference, DASFAA 2011, International Workshops: GDB, SIM3, FlashDB, SNSMW, DaMEN, DQIS, Hong Kong, China, April 22-25, 2011, Proceedings
    EditorsJianliang Xu, Ge Yu, Shuigeng Zhou, Rainer Unland
    Place of PublicationHeidelberg [Germany]
    Pages526 - 537
    Number of pages12
    ISBN (Electronic)9783642202445
    ISBN (Print)9783642202438
    Publication statusPublished - 2011
    EventInternational Workshop on Data Quality in Integration Systems (DQIS 2011) - Hong Kong, China
    Duration: 22 Apr 201122 Apr 2011
    Conference number: 4th

    Publication series

    NameLecture Notes in Computer Science
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    WorkshopInternational Workshop on Data Quality in Integration Systems (DQIS 2011)
    Abbreviated titleDQIS 2011
    CityHong Kong
    OtherFourth International Workshop on Data Quality in Integration Systems (DQIS2011)
    In conjunction with the 16th International Conference on Database Systems for Advanced Applications (DASFAA 2011)
    (All published papers are expected to be indexed by SCI, EI, and ISTP)
    Internet address


    • Health information portals
    • Machine learning
    • Metadata
    • Quality assessment

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