Framework for merging probabilistic knowledge bases

Abstract

Knowledge merging is of major concern in developing probabilistic expert systems. Each system provides a consistent probabilistic knowledge while the merged knowledge base is often inconsistent. Because of this reason, a wide range of approaches has been put forward to merge probabilistic knowledge bases. However, the input of the models is the set of possible probabilistic functions representing the original probabilistic knowledge bases. In this paper, we investigate a framework for merging probabilistic knowledge bases represented by the new form. To this aim, a process to merge probabilistic knowledge bases is introduced, several transformation methods for the representation of the original probabilistic knowledge base is presented, a set of merging operators is proposed, and several desirable logical properties are investigated and discussed.