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Volume 35 Issue 10
Oct.  2009
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Journal of Beijing University of Aeronautics and Astronautics  > 2009  >  35(10): 1254-1257.
Li Ming, Liu Lu, Wang Jun, et al. Approach to expert recommendation with multiple knowledge areas based on fuzzy text categorization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2009, 35(10): 1254-1257. (in Chinese)
Citation: Li Ming, Liu Lu, Wang Jun, et al. Approach to expert recommendation with multiple knowledge areas based on fuzzy text categorization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2009, 35(10): 1254-1257. (in Chinese)
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Approach to expert recommendation with multiple knowledge areas based on fuzzy text categorization

  • 1.

    School of Economics and Management, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

  • 2. Dept. of System Engineering of Engineering Technology, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

  • Received Date: 30 Nov 2008
  • Publish Date: 31 Oct 2009
    Abstract
  • Recommending an appropriate expert in knowledge management systems is an effective and efficient way to utilize tacit knowledge. To recommend the experts with multiple knowledge areas and identify the user-s knowledge needs completely, an approach to expert recommendation with multiple knowledge areas based on fuzzy text categorization was proposed. Firstly, fuzzy text classifier was constructed. An expert profile was built by classifying the newly registered knowledge artifacts fuzzily and with volume and time factors. Knowledge needs model was composed of explicit knowledge needs and implicit knowledge needs. Explicit knowledge needs was identified by analyzing the browsing logs. Implicit knowledge needs was measured by the entropy of the explicit knowledge needs. Expert was recommended based on the matching degree of expert profile and the user-s knowledge needs model. The proposed approach was developed and was used successfully in the knowledge management system. The approach was proved to be applicable.

     

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  • [1] Maybury M T. Expert finding systems . MTR06B000040, 2006 [2] Yang K W, Hu S Y. Automatic expert identification using a text categorization technique in knowledge management systems [J]. Expert Systems with Applications,2008,34(2):1445-1455 [3] Fang H, Zhai C. Probabilistic models for expert finding Amati G, Carpineto C, Romano G.Proceedings of the 29th European Conference on Information Retrieval. Berlin: Springer-Verlag, 2007:418-430 [4] Serdyukov P, Hiemstra D. Modeling documents as mixtures of persons for expert finding Macdonald C, Ounis I, Plachouras V, et al. Advances in Information Retrieval. Berlin: Springer-Verlag, 2008:309-320 [5] Balog K, Azzopardi L, Rijke M D A. Language modeling framework for expert finding [J]. Information Processing & Management, 2009,45(1):1-19 [6] Hughes A M. The complete database marketer: Second generation strategies and techniques for tapping the power of your customer database [M]. New York: McGraw-Hill, 1996
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