Automatic expert identification using a text categorization technique in knowledge management systems

Since tacit knowledge such as know-how and experiences is hard to be managed effectively using information technology, it is recently proposed that providing an appropriate expert identification mechanism in KMS to pinpoint experts in the organizations with searched expertise is more effective and efficient to utilize this type of knowledge. In this paper, we propose a framework to automate expert identification using a text categorization technique called Vector Space Model to minimize maintenance cost of expert profiles as well as problems related to incorrectness and obsolescence resulted from subjective manual profile processing. Also, we define the structure of expertise consisting of activeness, relevance, and usefulness factors to enable deriving the overall expertise level of experts by analyzing knowledge artifacts registered to the knowledge base. The developed prototype system, "Knowledge Portal for Researchers in Science and Technology", is introduced to show the applicability of the proposed framework. (C) 2007 Elsevier Ltd. All rights reserved.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2008-02
Language
English
Keywords

MODEL

Citation

EXPERT SYSTEMS WITH APPLICATIONS, v.34, no.2, pp.1445 - 1455

ISSN
0957-4174
DOI
10.1016/j.eswa.2007.01.010
URI
http://hdl.handle.net/10203/4420
Appears in Collection
MT-Journal Papers(저널논문)
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