Automatic configuration selection using ontology matching task profiling

Authors:
Isabel F. Cruz;Alessio Fabiani;Federico Caimi;Cosmin Stroe;Matteo Palmonari
Affiliations:
ADVIS Lab, Department of Computer Science, University of Illinois at Chicago;ADVIS Lab, Department of Computer Science, University of Illinois at Chicago;ADVIS Lab, Department of Computer Science, University of Illinois at Chicago;ADVIS Lab, Department of Computer Science, University of Illinois at Chicago;DISCo, University of Milan-Bicocca, Italy
Venue:
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Year:
2012

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Abstract

An ontology matching system can usually be run with different configurations that optimize the system's effectiveness, namely precision, recall, or F-measure, depending on the specific ontologies to be aligned. Changing the configuration has potentially high impact on the obtained results. We apply matching task profiling metrics to automatically optimize the system's configuration depending on the characteristics of the ontologies to be matched. Using machine learning techniques, we can automatically determine the optimal configuration in most cases. Even using a small training set, our system determines the best configuration in 94% of the cases. Our approach is evaluated using the AgreementMaker ontology matching system, which is extensible and configurable.