TítuloOn Similarity Measures based on a Refinement Lattice
Publication TypeConference Paper
Year of Publication2009
AuthorsOntañón S, Plaza E
EditorWilson LMcGinty Da
Conference NameICCBR'09: 8th International Conference on Case-Based Reasoning
Volume5650
EditorialLecture Notes in Artificial Intelligence, Springer Verlag
Conference LocationSeattle
Paginación240-255
Date Published20/07/2009
ISBN Number978-3-642-02997-4
Palabras claveCBR, feature logics, Similarity
Resumen

Retrieval of structured cases using similarity has been studied in CBR but there has been less activity on defining similarity on description logics (DL). In this paper we present an approach that allows us to present two similarity measures for feature logics, a subfamily of DLs, based on the concept of refinement lattice.The first one is based on computing the anti-unification (AU) of two cases to assess the amount of shared information. The second measure decomposes the cases into a set of independent properties, and then assesses how many of these properties are shared between the two cases. Moreover, we show that the defined measures are applicable to any representation language for which a refinement lattice can be defined. We empirically evaluate our measures comparing them to other measures in the literature in a variety of relational data sets showing very good results.