Bayesian network modeling based on CBR and RBR

The key of using Bayesian network is to correctly and efficiently construct models for problems. But, learning Bayesian network from data may be time expensive because of huge search space. A modeling method based on case-based reasoning (CBR) and rule-based reasoning (RBR) is proposed. We build a domain knowledge base and represent Bayesian networks by frame and first-order probability logic. When facing a new problem, we use similarity ratio and difference ratio to match cases, and then combine and prune candidate cases to form a new model. In the whole process, case-based reasoning is the main technique, and rule-based reasoning plays an assistant role. This method directly reuses historical cases so as to improve Bayesian network modeling efficiency.