Research On The Matching Of Supply And Demand Of Tacit Knowledge Resources In Knowledge Service Organizations

With the advent of the big data and 5G era,tacit knowledge has become an important resource for value creation and the main source of knowledge innovation.Reasonable use of tacit knowledge resources and effective improvement of their utilization rate will help to realize the effective allocation of tacit knowledge resources,and then maximize the value of knowledge resources.The rapid increase of data,information and tacit knowledge in the practice domain has caused the problems of low knowledge dissemination efficiency and low utilization rate to become increasingly prominent.Knowledge service organizations provide users with satisfactory knowledge services,which is the fundamental way to alleviate users'"knowledge fascination" and improve knowledge application and even innovation efficiency.In view of this,the thesis intends to conduct research on the matching of supply and demand of tacit knowledge resources in knowledge service organizations.In the process of matching the supply and demand of knowledge resources,the knowledge service organization traverses the existing knowledge base according to the knowledge needs of users;the passage of time and the in-depth application will lead to the continuous growth of the scale of the knowledge base,and the result will cause the bottleneck of knowledge retrieval and increase the time cost of tacit knowledge matching between supply and demand.In addition,the matching degree of knowledge supply and demand is an important indicator that affects user demand satisfaction.In the process of matching knowledge supply and demand,calculating the view similarity between existing knowledge and user needs is the prerequisite for knowledge service organizations to provide knowledge services.Herein,this thesis uses improved K-means algorithm to cluster tacit knowledge and compresses the traversal space.At the same time,in order to improve the accuracy of view similarity calculation results,the calculation method of view similarity is improved.First,by analyzing different clustering algorithms,this thesis selects the K-means algorithm as the basic algorithm and aims at its shortcomings,and implements improvements to the K-means algorithm by introducing the aggregation distance.Among them,the initial clustering center is determined according to the aggregation distance parameter,which effectively reduces the possibility of misclassification due to random selection;at the same time,considering the difference between different clusters in the clustering process,the DBI(Davies Bouldin Index)indicator function is used as the criterion function of the improved K-means algorithm.In this way,clustering analysis of tacit knowledge through the improved K-means algorithm can effectively improve the accuracy of the clustering results of explicit cases of tacit knowledge,compress the traversal space,and improve the efficiency of tacit knowledge matching between supply and demand.Then,considering the impact of the weight vector of the explicit case condition attribute set of the tacit knowledge on the view similarity calculation and even the result of knowledge supply and demand matching,this thesis analyzes the advantages and disadvantages of the existing weighting methods,and selects the entropy weight method and the CRITIC weight method(Criteria Importance Through Intercriteria Correlation)calculates the condition attribute weight of the explicit case of tacit knowledge.At the same time,based on the principle of "complementary advantages",the two weighting methods are combined,and combined weighting is achieved through the method of maximizing dispersion.This method can effectively improve the scientificity of case view similarity calculation and ensure the matching effect of the supply and demand of the explicit case of tacit knowledge.In view of the fact that in practice,knowledge services are usually terminated due to view similarity.In order to solve this problem,the thesis uses preset view similarity thresholds to adapt similar cases that meet the conditions to improve the knowledge service.Utilization of quality and tacit knowledge.Based on this,an algorithm for matching supply and demand of explicit cases of tacit knowledge in knowledge service organizations is proposed.Finally,select the University of California,Irvine UCI standard test database Winequanlity data set for experimental analysis.During the experiment,the similarity of the explicit case view of the tacit knowledge was calculated according to the user's knowledge requirements,and the matching result was determined accordingly.Then,the matching efficiency of the required matching algorithm provided in the thesis and the general algorithm are compared.The experimental results verify the effectiveness and feasibility of the tacit knowledge supply-demand matching algorithm proposed in this thesis.