Relational instance-based learning with lists and terms
Machine Learning - Special issue on inducive logic programming
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Machine Learning
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Similarity Measures for Object-Oriented Case Representations
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Structural Similarity as Guidance in Case-Based Design
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
Relational Case-based Reasoning for Carcinogenic Activity Prediction
Artificial Intelligence Review
Inductive inference of VL decision rules
ACM SIGART Bulletin
The Explanatory Power of Symbolic Similarity in Case-Based Reasoning
Artificial Intelligence Review
Amalgams: a formal approach for combining multiple case solutions
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Measuring similarity in description logics using refinement operators
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
Efficient operations in feature terms using constraint programming
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Feature term subsumption using constraint programming with basic variable symmetry
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
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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.