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Finding academic experts on a multisensor approach using Shannon’s entropy

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posted on 2020-06-10, 16:07 authored by C Moreira, A Wichert

Expert finding is an information retrieval task concerned with the search for the most knowledgeable people, in some topic, with basis on documents describing peoples activities. The task involves taking a user query as input and returning a list of people sorted by their level of expertise regarding the user query. This paper introduces a novel approach for combining multiple estimators of expertise based on a multisensor data fusion framework together with the Dempster–Shafer theory of evidence and Shannon’s entropy. More specifically, we defined three sensors which detect heterogeneous information derived from the textual contents, from the graph structure of the citation patterns for the community of experts, and from profile information about the academic experts. Given the evidences collected, each sensor may define different candidates as experts and consequently do not agree in a final ranking decision. To deal with these conflicts, we applied the Dempster–Shafer theory of evidence combined with Shannon’s Entropy formula to fuse this information and come up with a more accurate and reliable final ranking list. Experiments made over two datasets of academic publications from the Computer Science domain attest for the adequacy of the proposed approach over the traditional state of the art approaches. We also made experiments against representative supervised state of the art algorithms. Results revealed that the proposed method achieved a similar performance when compared to these supervised techniques, confirming the capabilities of the proposed framework.


    Funding

    This work was supported by Fundação para a Ciência e Tecnologia (FCT): PTDC/EIA-CCO/119722/2010 and by Fundação para a Ciência e Tecnologia FCT (INESC-ID multiannual funding) through the PIDDAC Program funds.

    History

    Citation

    Expert Systems with Applications, 2013, 40 (14), pp. 5740-5754

    Author affiliation

    /Organisation/COLLEGE OF SOCIAL SCIENCES, ARTS AND HUMANITIES/School of Management

    Version

    • AM (Accepted Manuscript)

    Published in

    Expert Systems with Applications

    Volume

    40

    Issue

    14

    Pagination

    5740-5754

    Publisher

    Elsevier

    issn

    0957-4174

    Copyright date

    2013

    Language

    en

    Publisher version

    https://www.sciencedirect.com/science/article/pii/S0957417413002388

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