Abstract
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 refinement operators and 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 most specific concepts, then the least common subsumer of these two concepts, and finally measuring their distances in the refinement graph.
Partially supported by the Spanish Ministry of Science and Education project Next-CBR (TIN2009-13692-C03-01 and TIN2009-13692-C03-03).
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Sánchez-Ruiz, A.A., Ontañón, S., González-Calero, P.A., Plaza, E. (2011). Measuring Similarity in Description Logics Using Refinement Operators. In: Ram, A., Wiratunga, N. (eds) Case-Based Reasoning Research and Development. ICCBR 2011. Lecture Notes in Computer Science(), vol 6880. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23291-6_22
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