Enterprise Knowledge Preservation and Management

Enterprise Knowledge Preservation and Management

Charalampos Chelmis (University of Southern California, USA), Vikram Sorathia (University of Southern California, USA) and Viktor K. Prasanna (University of Southern California, USA)
DOI: 10.4018/978-1-4666-4478-6.ch002


The decision making process in organizations is constantly evolving with expanding geographical boundaries and ever-changing technology landscape. A major part of decisions and deliberations now typically takes place in collaboration platforms like emails, enterprise social networks, discussion servers, chats, and conferencing services. These platforms contain problem solving insights, recommendations, best practices, expert opinions, and answers, and must be considered part of the organizational knowledge management effort. However, traditional knowledge management techniques do not sufficiently capture the hidden nuggets of knowledge buried in communication logs. In this chapter, the authors describe the need for a paradigm shift in knowledge management strategy and propose semantic social network analysis as a potential solution. They introduce the concept of social knowledge networks and describe knowledge algebra by defining rigorous social metrics. Finally, to demonstrate the applicability of the approach, the authors provide two case studies that lead to identification of experts and mining of best practices from informal communication at the workplace.
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A knowledge base is a system that enables domain knowledge collection, organization and retrieval. The Artificial Intelligence community has widely used knowledge bases in order to represent knowledge using rich modeling languages. Formal knowledge modeling and representation in the form of logic rules makes knowledge consumable to machines, enabling previously unknown and/or non-obvious knowledge generation through automated deductive reasoning. Semantic Web technologies use formal schemata instead of rules to capture the structure of stored data (i.e., entity types and relationships between them), and exploit such representation to discover non-obvious relationships between nodes using inferencing.

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