Research On Matching Between Supply And Demand Of Tacit Knowledge Based On Fuzzy Case-based Reasoning

With tacit knowledge between the various innovation subjects increasing,the competitiveness of enterprises relies more on tacit knowledge.However,the transferring efficiency of tacit knowledge in the main innovation subjects is not high,mainly due to the supply and demand information of the tacit knowledge subjects not matching,and it needs to improve the matching efficiency of the tacit knowledge.According to the degree of the knowledge expression difficulty,tacit knowledge is divided into shallow tacit knowledge and deep tacit knowledge.Shallow knowledge expressed more easily than deep tacit knowledge,and is used more easily within the enterprise business.Based on this,in order to improve the efficiency of tacit knowledge matching,this article was from the shallow tacit knowledge,and used fuzzy reasoning method to study the supply and demand matching of tacit knowledge.The specific content of this paper include: questions raised background,purpose,significance,research innovation,research methods and technology ways.The theoretical part of the shallow tacit knowledge involves matching supply and demand theory,and the theoretical framework building process of tacit knowledge supply and demand matching.Based on the element model of the shallow expression of tacit knowledge,there was an overview of the advantages and disadvantages of common knowledge representation methods,and a detailed explanation of the use of matter-element model.Build and manage shallow tacit knowledge case base,the process described in detail and build cases based on AHP and knowledge guide law.The result shows that using specific examples to verify the fuzzy case-based reasoning algorithm and entropy application in shallow tacit knowledge matching supply and demand,summary and outlook,conclusions and specifically addressed further research work.