A New Approach for Rule-Based Knowledge Value-Added Treatment Inference

During knowledge accumulation, various knowledge sources and various expert comments in the knowledge base, lead to knowledge overlapping, conflict or different data size in the knowledge base, and as change of time and space, may cause knowledge inapplicability, and wrong knowledge would lead to wrong decision. This study proposed using reliability factor theory to express knowledge conflict, overlapping or variable data size. Based on knowledge correlation, the rule-based knowledge value added treatment algorithm is set up to run value added treatments such as merging, integrating, deleting, innovating and appending, so that the knowledge becomes more integral, correlative mapping and reliability can be exhibited in concrete, and wrong decisions can be avoided.