Formalizing partial matching and similarity in case-based reasoning with a description logic
Pascal Coupey, Christophe Fouqueré, Sylvie Salotti · Applied Artificial Intelligence · 1998
Our aim is to use a description logic including default delta and exception epsilon connectives as a formal framework for a case-based reasoning (CBR) system. This approach allows the retrieval of similar cases to be formalized. Subsumption and (sure, probable, typical, and exceptional) inheritance relations of the description logic are the foundations for the different retrieval tasks: abstracting the new case; classifying it in the index base (full and partial matching); evaluating similarity of the conceptual abstraction of the new case with the concepts of the index base, using conceptual preference criteria; and retrieving similar cases (instances) and applying instance preference criteria to order them. Our preference criteria are symbolic rather than numerical or those of fuzzy logic. Using description logic offers several advantages: the classification process can be used to retrieve similar cases, the formal properties and the efficiency of the system can easily be evaluated, and preference criteria are homogeneously based on the formal description logic framework. Moreover, preference criteria are independent of the knowledge and can thus be used in other applications.