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Using Machine Learning to Support Resource Quality Assessment: An Adaptive Attribute-Based Approach for Health Information Portals

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Database Systems for Adanced Applications (DASFAA 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6637))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Center for Organizational and Social Informatics (COSI), Monash University, 900 Dandenong Road, Caulfield East, Australia

    Jue Xie & Frada Burstein

Authors
  1. Jue Xie
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  2. Frada Burstein
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, KLN, Hong Kong, China

    Jianliang Xu

  2. School of Information Science and Engineering, Northeastern University, Shenyang, 110004, Liaoning, China

    Ge Yu

  3. School of Computer Science, Fudan University, 220 Handan Road, 200433, Shanghai, China

    Shuigeng Zhou

  4. Institute for Computer Science and Business Information Systems (ICB), University of Duisburg-Essen, Schützenbahn 70, 45117, Essen, Germany

    Rainer Unland

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© 2011 Springer-Verlag Berlin Heidelberg

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Xie, J., Burstein, F. (2011). Using Machine Learning to Support Resource Quality Assessment: An Adaptive Attribute-Based Approach for Health Information Portals. In: Xu, J., Yu, G., Zhou, S., Unland, R. (eds) Database Systems for Adanced Applications. DASFAA 2011. Lecture Notes in Computer Science, vol 6637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20244-5_50

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  • DOI: https://doi.org/10.1007/978-3-642-20244-5_50

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20243-8

  • Online ISBN: 978-3-642-20244-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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