Approach to Searching Multiple Case Bases Based on Bayesian Belief Network

To overcome the limitation of single case base retrieval,an approach to searching multiple case bases based on the Bayesian belief network is proposed.Keywords are adapted to each case base to describe the target case.The matching degree between the Key words and the features are derived from the Bayesian belief network.The best match is used to search the case base.The liner normalization method is then used to normalize the similarity degree between the target case and the retrieved cases.The final similarity degree is derived using the normalized similarity degree and the matching degree of the target description.Consequently,retrieved cases ranked in final similarity degree are returned.Results of the application and experiment in the aeronautical enterprise show that the proposed approach is efficient to search multiple case bases.