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Identifying communities of practice through ontology network analysis

Alani, Harith; Dasmahapatra, Srinandan; O'Hara, Kieron and Shadbolt, Nigel (2003). Identifying communities of practice through ontology network analysis. IEEE Intelligent Systems, 18(2) pp. 18–25.

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Communities of practice--groups of individuals interested in a particular job, procedure, or work domain--informally swap insights on work-related tasks, often through quick chats by the water cooler. They act as corporate memories, transfer best practice, provide mechanisms for situated learning, and act as foci for innovation.1,2 Increasingly, organizations are harnessing communities of practice to carry out important knowledge management functions.3 However, a significant first step is identifying the community, which often doesn?t designate itself as such, and its members, who don?t know they belong? So, this step involves determining which people in a community of practice have common interests in particular practices or functions and producing sets or clusters of related individuals. Community identification traditionally demands heavy resources and often includes extensive interviewing.In this article, we describe Ontocopi (Ontology-Based Community of Practice Identifier), a tool to help identify communities. Ontocopi lets you infer the informal relations that define a community of practice from the presence of more formal relations. For instance, if A and B have no formal relation but they have both authored papers with C (formal relation), they might share interests (informal relation). Because Ontocopi works in this way, we cannot claim without qualification that it identifies communities of practice. Significant informal relations might have little or no connection to the formal ones. Here, we refer to the networks uncovered by Ontocopi as COPs and to informal social networks as communities of practice. We work under the assumption that COPs are sometimes decent proxies for communities of practice.

Item Type: Journal Item
Copyright Holders: 2003 IEEE
ISSN: 1541-1672
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Item ID: 20053
Depositing User: Harith Alani
Date Deposited: 15 Apr 2010 12:54
Last Modified: 07 Dec 2018 19:41
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