Measuring Similarity in Description Logics using Refinement Operators

Similarity assessment is a key operation in many artificial intelligence fields, such as case-based reasoning, instance-based learning, ontology matching, clustering, etc. This paper presents a novel measure for assessing similarity between individuals represented using Description Logic (DL). We will show how the ideas of {\em refinement operators} and {\em refinement graph}, originally introduced for inductive logic programming, can be used for assessing similarity in DL and also for abstracting away from the specific DL being used. Specifically, similarity of two individuals is assessed by first computing their {\em most specific concepts}, then the {\em least common subsumer} of these two concepts, and finally measuring their distances in the refinement graph