Abstract
We consider the problem of discovering pairs of similar concepts, which are part of two given source ontologies, in which each concept node is mapped to a set of instances. The similarity measures we propose are based on learning a classifier for each concept that allows to discriminate the respective concept from the remaining concepts in the same ontology. We present two new measures that are compared experimentally: (1) one based on comparing the sets of support vectors from the learned SVMs and (2) one which considers the list of discriminating variables for each concept. These lists are determined using a novel variable selection approach for the SVM. We compare the performance of the two suggested techniques with two standard approaches (Jaccard similarity and class-means distance). We also present a novel recursive matching algorithm based on concept similarities.
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Todorov, K., Geibel, P., Kühnberger, KU. (2010). Mining Concept Similarities for Heterogeneous Ontologies. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2010. Lecture Notes in Computer Science(), vol 6171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14400-4_7
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DOI: https://doi.org/10.1007/978-3-642-14400-4_7
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