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Use of RDF for expertise matching within academia

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Abstract

Organisations have realized that effective development and management of their organisational knowledge base is critical to survival in today’s competitive business environment. Employees, as a special knowledge asset, also attract the interest of many researchers because only through people communicating with one another can they really share their tacit knowledge and skills, which are often more valuable than sharing explicit documentations. The need to be able to quickly locate experts among the heterogeneous data sources stored in the organisational memory has been recognized by many researchers. This paper examines the advantages of using RDF (resource description framework) for expertise matching—the process of finding an individual with the required knowledge and skills. The major challenge is to semantically integrate multiple expertise indications from heterogeneous data sources stored in the organisational memory in order to facilitate users to locate the right expert(s). To understand the issues, a case study has been performed that involves building a RDF-based expertise broker that helps Ph.D. applicants locate potential supervisors before they formally apply to a university. An evaluation of the brokering system has been conducted through an experiment and the key results are presented. An extended brokering system that would support expertise matching in a multidisciplinary context is also described and further research identified.

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  1. Informatics Research Institute, School of Computing, University of Leeds, Leeds, United Kingdom, LS2 9JT

    Ping Liu, Jayne Curson & Peter Dew

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  1. Ping Liu
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  2. Jayne Curson
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  3. Peter Dew
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Correspondence to Ping Liu.

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Liu, P., Curson, J. & Dew, P. Use of RDF for expertise matching within academia. Knowl Inf Syst 8, 103–130 (2005). https://doi.org/10.1007/s10115-004-0152-y

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