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Knowledge Perception Analysis in a Social Network

Knowledge management in organizations is gaining in importance, especially with the advent of computer networks. Networks foster interaction between individuals, and have become the medium of choice for all types of interactions, both professional and social. In this research, we study the perception of knowledge in an organization’s email network. An important aspect of an individual’s knowledge is that it may be incomplete and hence any analysis approach must handle knowledge uncertainty. We propose an approach based on the Dempster-Shafer theory of evidence for modeling individuals’ perceptions about knowledge, thus enabling the understanding of knowledge in an organization. We show how correlating the knowledge of two or more individuals can help identify the discrepancies between them, and thus identify sources of organizational misperceptions. The proposed approach has been evaluated on the publicly available e-mail logs from the Enron Corporation. For the present study, meaning extraction from e-mail content was done manually. Initial results show that the approach is very promising. Our continuing research is focusing on applying techniques for automated identification of knowledge from email as well as sentiment analysis techniques for automated evaluation of individuals’ sentiments.