Resolving Inconsistencies in Probabilistic Knowledge Bases by Quantitative Modification

Abstract

Resolving the inconsistency that appears in knowledge bases is an extremely important stage in the process of merging knowledge bases. With probabilistic context, many studies have solved the inconsistency of a probabilistic knowledge base (PKB) with different approaches. However, these previous focus on addressing the problem of restoring consistency in a PKB by employing the principles of maximum entropy or building the consistency restorer with complex creeping functions. We discover that it is really difficult to find the functions that are utilized in the creeping consistency restorers. Therefore, the process of finding these functions may lead to a poor performance. Based on that, we proposed the inconsistency solvers for a PKB, where we consider revising the functions involved in handling the inconsistency in a simpler direction. Two the inconsistency solvers are the equitable deformation inconsistency solver and the amerced deformation inconsistency solver. Corresponding to each solver, an algorithm to solve inconsistencies in a PKB is proposed. Moreover, the cost of each algorithm also are considered and proved.