A Knowledge Management Optimization Problem Using Marginal Utility in a Metric Space with Conceptual Graphs

摘要

Knowledge management has emerged as a field blending a systems approach with methods drawn from organizational management and learning. In contrast, knowledge representation, a branch of artificial intelligence, is grounded in formal methods. Research in the separate behavioral and the structural disciplines - knowledge management and knowledge engineering -have not traditionally cross-pollinated, preventing the development of many practical uses. Organization managers lack guidance in where to direct improvement efforts targeted at specific groups of knowledge workers. Demonstrated here is Knowledge Improvement Measurement System, an optimization solution that employs marginal utility theory in a metric space, and formal reasoning via software agents realized in conceptual graphs. This allows for repeated evaluation of knowledge improvement measurements. The KIMS method can measure activities that organize and encourage knowledge sharing to achieve competitive advantage. The solution takes into account the body of knowledge related to human understanding and learning, and formal methods of knowledge organization.