Jae Kwang Lee

A case-based reasoning approach to cognitive map-driven tacitknowledge management

Abstract Knowledge is at the heart of knowledge management. In literature, a lot of studies have been suggested covering the role of knowledge in improving the performance of management. However, there are few studies about investigating knowledge itself in the arena of knowledge management. Knowledge circulating in an organization may be explicit or tacit. Until now, literature in knowledge management shows that it has mainly focused on explicit knowledge. On the other hand, tacit knowledge plays an important role in the success of knowledge management. It is relatively hard to formalize and reuse tacit knowledge. Therefore, research proposing the explication and reuse of tacit knowledge would contribute significantly to knowledge management research. In this sense, we propose using cognitive map (CM) as a main vehicle of formalizing tacit knowledge, and case-based reasoning as a tool for storing CM-driven tacit knowledge in the form of frame-typed cases, and retrieving appropriate tacit knowledge from the case base according to a new problem. Our proposed methodology was applied to a credit analysis problem in which decision-makers need tacit knowledge to assess whether a firm under consideration is healthy or not. Experiment results showed that our methodology for tacit knowledge management can provide decision makers with robust knowledge-based support.

A methodology for modeling influence diagrams: a case-based reasoning approach

In this paper, a case-based reasoning approach to build an influence diagram is described. Building an influence diagram in decision analysis is known to be a most complicated and burdensome process. To overcome such a difficulty, decision class analysis is suggested, which treats a set of decisions having some degree of similarity as a single unit. This research suggests a case-based reasoning approach as a methodology to analyze a class of decisions. The candidate influence diagrams are retrieved from a set of similar influence diagrams, a case base. They are combined and modified by the node classification tree and DM’s preference for the given decision problem. For such a purpose, the case representation and retrieval process is explained with the adaptation process. We suggest using two measure, the fitness and garbage ratio for the case retrieval process. The basic concept of decision class analysis and case-based reasoning is very similar so the case-based reasoning approach is believed to be a better methodology to implement a decision class analysis. Copyright