Graph-based cluster analysis to identify similar questions: A design science approach

Blooma JOHN, Dion Goh, Alton Chua, Nilmini Wickramasinghe

    Research output: Contribution to journalArticle

    6 Citations (Scopus)

    Abstract

    Social question answering (SQA) services allow users to clarify their queries by asking questions and obtaining answers from other users. To enhance the responsiveness of such services, one can identify similar questions and, thereafter, return the answers available. However, identifying similar questions is difficult because of the complex language structure of user-generated questions. For this reason, we developed an approach to cluster similar questions based on a web of social relationships among the questions, the answers, the askers, and the answerers. To do so, we designed a graph-based cluster analysis using design science research guidelines. In evaluating the results, we found that the proposed graph-based cluster analysis is more promising than baseline methods.
    Original languageEnglish
    Pages (from-to)590-613
    Number of pages24
    JournalJournal of the Association of Information Systems
    Volume17
    Issue number9
    Publication statusPublished - 2016

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    JOHN, Blooma ; Goh, Dion ; Chua, Alton ; Wickramasinghe, Nilmini. / Graph-based cluster analysis to identify similar questions: A design science approach. In: Journal of the Association of Information Systems. 2016 ; Vol. 17, No. 9. pp. 590-613.
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    Graph-based cluster analysis to identify similar questions: A design science approach. / JOHN, Blooma; Goh, Dion; Chua, Alton; Wickramasinghe, Nilmini.

    In: Journal of the Association of Information Systems, Vol. 17, No. 9, 2016, p. 590-613.

    Research output: Contribution to journalArticle

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    T1 - Graph-based cluster analysis to identify similar questions: A design science approach

    AU - JOHN, Blooma

    AU - Goh, Dion

    AU - Chua, Alton

    AU - Wickramasinghe, Nilmini

    PY - 2016

    Y1 - 2016

    N2 - Social question answering (SQA) services allow users to clarify their queries by asking questions and obtaining answers from other users. To enhance the responsiveness of such services, one can identify similar questions and, thereafter, return the answers available. However, identifying similar questions is difficult because of the complex language structure of user-generated questions. For this reason, we developed an approach to cluster similar questions based on a web of social relationships among the questions, the answers, the askers, and the answerers. To do so, we designed a graph-based cluster analysis using design science research guidelines. In evaluating the results, we found that the proposed graph-based cluster analysis is more promising than baseline methods.

    AB - Social question answering (SQA) services allow users to clarify their queries by asking questions and obtaining answers from other users. To enhance the responsiveness of such services, one can identify similar questions and, thereafter, return the answers available. However, identifying similar questions is difficult because of the complex language structure of user-generated questions. For this reason, we developed an approach to cluster similar questions based on a web of social relationships among the questions, the answers, the askers, and the answerers. To do so, we designed a graph-based cluster analysis using design science research guidelines. In evaluating the results, we found that the proposed graph-based cluster analysis is more promising than baseline methods.

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    KW - Design Science

    KW - Graph Theory

    KW - Social Question Answering

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