Research on Intensive Facts about Explicit Case of Tacit Knowledge

In the current knowledge-based economy, the role of knowledge resources in cultivating the core competitiveness of enterprises has become increasingly prominent, especially those difficult to encode, and highly personalized tacit knowledge, but also play a multiplier role. Tacit knowledge determines the effective level of human knowledge application and knowledge innovation. Attribute reduction is an important part of the construction of case knowledge system. In this paper, through several methods of attribute reduction, they are AHP, PCA, CV, Entropy Method and RS, comparative analysis them from the method theory, advantages and disadvantages, applicable objects and areas of application, combined with the characteristics of tacit knowledge itself index data, the rough set is innovatively applied to the tacit knowledge dominance case, and uses this theory to carry on the attribute reduction to the case storehouse carries on the numerical simulation, but also proved that the algorithm more in line with the practical application needs, but also more feasible and effective.