Mining Unnoticed Knowledge in Collaboration Support Systems

Mining Unnoticed Knowledge in Collaboration Support Systems

George Gkotsis (University of Patras, Greece) and Nikos Tsirakis (University of Patras, Greece)
DOI: 10.4018/978-1-60566-711-9.ch007


Numerous tools aiming at facilitating or enhancing collaboration among members of diverse communities have been already deployed and tested over the Web. Focusing on the particularities of online communities of practice (CoPs), this chapter introduces a framework for mining knowledge that is hidden in such settings. The authors’ motivation stems from the criticism that contemporary tools receive regarding lack of active participation and limited engagement in their use, which is partially due to the inability of identifying and exploiting a set of important relationships among community members and the associated collaboration-related assets. The authors’ overall approach elaborates and integrates issues from the data mining and the social networking disciplines. More specifically, the proposed framework enables CoPs members to rank the contributions of their peers towards identifying meaningful relationships, as well as valuable information about roles and competences. In the context of this chapter, the authors first model the characteristics of the overall collaboration setting and propose a set of associated metrics. Next, in order to reveal unnoticed knowledge which resides within CoPs, a data mining technique that groups users into clusters and applies advanced social networking analysis on them is proposed. Finally, the authors discuss the benefits of their approach and conclude with future work plans.
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Contemporary approaches to online environments hosting a large group of users build on diverse user profiling mechanisms (Fink & Kobsa, 2001). These approaches usually distinguish between static (or user defined) and dynamic (a set of attributes updated by the system) user profiles. Dynamic attributes derive by tracking down user actions and aim at providing a more personalized environment. Personalization of the environment may include user interface adaptation by making most usable actions or information more easily accessible. Moreover, by taking into account a user's profile, these approaches aim at filtering information which generally resides in a big collection of documents. Information filtering is achieved by reading the content of these documents and combining its content with the user's profile. The main goal of these approaches is to provide individualized recommendations to users concerning the system items (Burke, 2002).

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