A Study Of Tacit Knowledge Explicit Case Adaptation

In the era of knowledge economy,tacit knowledge has become the core resource of value creation for organizations.The realization of the value of tacit knowledge requires the support of case matching as well as case adaptation.However,in practical applications,most case based reasoning(CBR)systems only provide early-stage retrieval services for knowledge users,and the subsequent knowledge adaptation lacks the proper system support.Obviously,the system completely leaves the case adaptation work to the knowledge users,which will not only increase the workload of the knowledge users,but also restrict the applying effectiveness of knowledge,and the system will not to act if it fails to match.Therefore,it is necessary to provide complete process support for knowledge users to ensure the effectiveness of the application of knowledge.Based on this,in order to improve the application effectiveness of knowledge,this paper studies the case adaptation of explicit knowledge,and designs a multi-case induced adaptation method based on the combination of improved K-nearest neighbor(KNN)algorithm and genetic algorithm(GA)-C4.5 decision tree algorithm to provide a solving method based on a non-zero basis for knowledge users.First,an improved KNN algorithm is used to determine the adaptation set of the problem to be solved.In order to overcome the shortcomings of the classical KNN algorithm,a KNN algorithm for partitioning clustering regions based on combined weighting is proposed.In this algorithm,the rough set-entropy weight combination weighting algorithm is used to calculate the explicit case view of the tacit knowledge,and then the fuzzy C-means(FCM)clustering area division algorithm is used to divide the cases in the case base library.Divide to make the retrieval results more practical and improve the retrieval efficiency of the algorithm,and finally the retrieval results will be used as the basis for the solution of adaptive solution trajectory.Then,the GA-C4.5 algorithm is used to solve the adaptive solution trajectory ofthe problem to be solved and the induced adaptation is implemented.For the situation that cases often have multiple decision-making aspects,the C4.5 algorithm is used to solve the adaptive solution trajectory of the problem to be solved,but its efficiency depends on the number of conditions.Because the rough set algorithm based on GA can reduce the redundancy better,the GA reduction algorithm was introduced into the C4.5 algorithm to remove the redundant condition in the knowledge expression system,and then the solution efficiency of the algorithm will be improved.Finally,the winequality-white data set in the University of California Irvine(UCI)database was used as the data for the case analysis,the effectiveness and efficiency of the multi-case induced adaptation algorithm constructed in this paper is analyzed and verified.