Learning to map between ontologies on the semantic web
Proceedings of the 11th international conference on World Wide Web
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Facilitating the Exchange of Explicit Knowledge through Ontology Mappings
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
An introduction to variable and feature selection
The Journal of Machine Learning Research
Dimensionality reduction via sparse support vector machines
The Journal of Machine Learning Research
Variable selection using svm based criteria
The Journal of Machine Learning Research
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Ontology Matching
FCA-MERGE: bottom-up merging of ontologies
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
An empirical study of instance-based ontology matching
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Extensional Ontology Matching with Variable Selection for Support Vector Machines
CISIS '10 Proceedings of the 2010 International Conference on Complex, Intelligent and Software Intensive Systems
A framework for a fuzzy matching between multiple domain ontologies
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
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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.