The Evolution Study On Explicit Case Of Tacit Knowledge

Knowledge has become the core competitive resource in the era of knowledge economy.Knowledge has high environmental dependence.In today's rapidly and dramatically changing environment,the old knowledge is pushed into the landfill at a high speed as well as the new knowledge coming out.High value of knowledge depends on its long-term maintenance of high quality.Therefore,effective evolution is required.Both research and practice show that tacit knowledge occupies the majority of knowledge structure and is of significant to practicality and innovation.The case externalization of tacit knowledge can promote the efficient and direct management.However,the rapid generation and arbitrary storage lead to the scale increasing and the redundancy of KMS,which seriously affect the efficiency and effectiveness.In view of this,from the time node of evolution,this paper proposes Periodic evolution model that can be carried out regularly,large scale and automatically,and Applied evolution model that can be targeted,high-quality and human-computer interaction.The two evolutionary models are parallel.The classification operation strategy is used to improve the quality and quantity,so as to improve the overall value under the limited scale of the cases.In the strategy of tacit knowledge evolution,the Periodic evolution model takes the case of daily operation and dormant database as the object.The case is preliminarily clustered according to the decision attribute,then filter based on the scarcity,and combined with timeliness and retrieval using standard threshold.The information gain which can fit the characteristics of tacit knowledge cases is used as attribute weight,and then the case similarity is calculated by Euclidean distance.Based on this,carrying out evolutionary operations such as elimination,dormancy,or activation,and the rationality of the model was verified.The Applied evolutionary model is guided by question cases(quasi-cases).Through the monitoring and filtering mechanism,the problem cases are classified according to the outliers,the similarity degree of adaptive source cases and the evaluation effect,Then discovering and storing the new cases in time with the domain experts as the evolution leader.In addition,the standard values are set and adjusted according to characteristics of user.This evolutionary has good flexibility and can be widely used for maintenance of various knowledge bases.Knowledge evolution is the last link in the chain of knowledge.It is not only the continuation of the original knowledge,but also the source of creating new knowledge.Maintaining quality cases is beneficial to improving the quality and the efficiency of solving problem in KMS,accelerating the application process of new knowledge in the organization,improving the effect and ensuring its sustainable development.