Extensional Ontology Matching with Variable Selection for Support Vector Machines

Abstract : The paper builds on a previous finding of the same authors that concept similarity can be measured on the basis of small sets of characteristic features, selected separately and independently for every concept of two source ontologies. Extending a previously defined parameter-dependent similarity measure, the paper suggests the application of parameter-free correlation coefficients as concept similarity measures and compares their performance with the performance of the parametric similarity measure. An overall procedure for extensional ontology matching based on the suggested similarity criteria is proposed and empirically tested. In addition, the work includes an evaluation of a novel variable selection technique based on Support Vector Machines (SVMs).
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Contributor : Konstantin Todorov <>
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Konstantin Todorov, Peter Geibel, Kai-Uwe Kuehnberger. Extensional Ontology Matching with Variable Selection for Support Vector Machines. 2010 International Conference on Complex, Intelligent and Software Intensive Systems (CISIS), Feb 2010, Krakow, Poland. pp.962-967, ⟨10.1109/cisis.2010.59⟩. ⟨hal-01987784⟩



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