Development of a Composite Knowledge Manipulation Tool: K-Expert

A goal of this study is to develop a Composite Knowledge Manipulation Tool (CKMT). Some of traditional medical activities are rely heavily on the oral transfer of knowledge, with the risk of losing important knowledge. Moreover, the activities differ according to the regions, traditions, experts’ experiences, etc. Therefore, it is necessary to develop an integrated and consistent knowledge manipulation tool. By using the tool, it will be possible to extract the tacit knowledge consistently, transform different types of knowledge into a composite knowledge base (KB), integrate disseminated and complex knowledge, and complement the lack of knowledge. For the reason above, I have developed the CKMT called as K-Expert and it has four advanced functionalities as follows. Firstly, it can extract/import logical rules from data mining (DM) with the minimum of effort. I expect that the function can complement the oral transfer of traditional knowledge. Secondly, it transforms the various types of logical rules into database (DB) tables after the syntax checking and/or transformation. In this situation, knowledge managers can refine, evaluate, and manage the huge-sized composite KB consistently with the support of the DB management systems (DBMS). Thirdly, it visualizes the transformed knowledge in the shape of decision tree (DT). With the function, the knowledge workers can evaluate the completeness of the KB and complement the lack of knowledge. Finally, it gives SQL-based backward chaining function to the knowledge users. It could reduce the inference time effectively since it is based on SQL query and searching not the sentence-by-sentence translation used in the traditional inference systems. The function will give the young researchers and their fellows in the field of knowledge management (KM) and expert systems (ES) more opportunities to follow up and validate their knowledge. Finally, I expect that the approach can present the advantages of mitigating knowledge loss and the burdens of knowledge transformation and complementation.