@conference {3400, title = {On Similarity Measures based on a Refinement Lattice}, booktitle = {ICCBR{\textquoteright}09: 8th International Conference on Case-Based Reasoning}, volume = {5650}, year = {2009}, month = {20/07/2009}, pages = {240-255}, publisher = {Lecture Notes in Artificial Intelligence, Springer Verlag}, organization = {Lecture Notes in Artificial Intelligence, Springer Verlag}, address = {Seattle}, abstract = {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.}, keywords = {CBR, feature logics, Similarity}, isbn = {978-3-642-02997-4}, author = {Santiago Onta{\~n}{\'o}n and Enric Plaza}, editor = {David C. Wilson, Lorraine McGinty} }