Abstract: CBR(Case-based Reasoning) is a branch of artificial intelligence,which overcomes the bottleneck of know-ledge acquisition.Case adaptation is a key step in CBR.Description logic ALC has been fully applied to the CBR,but now no alogirhtm is more effective to determine whether a retrieved similar case needs to be modified and how to fix based on description logic.ALC becomes ALCQ(D) as it introduces a qualitative quantity Q and a type constraint domain D.The algorithm in this paper uses ALCQ (D) concept to represent the source case and target case.Firstly,it presupposes that the retrieved source example can solve the problem of target,which means the target and source case examples both satisfy KB(knowledge base),but this may lead to inconsistent with KB.Then according to the conflict detection in the algorithm,it finds the concepts which lead to inconsistent in source concept instances and finally uses concept of replacement rules defined in this article to retrieve the most similar concepts to the inconsistent concept in ontology repository for replacing itself.Studies show that this algorithm has boundaries,reliability and completeness.This paper used an example to illustrate this algorithm.The results show that it can revise the similar case to solve the target problem.

Key words: Case-based reasoning,Description logic,Case adaptation,Qualified number restrictions,Concrete domain