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.
Research partially supported by NSF Awards IIS-0812258 and IIS-1143926 and by the Intelligence Advanced Research Projects Activity (IARPA) via Air Force Research Laboratory (AFRL) contract number FA8650-10-C-7061. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, AFRL, or the U.S. Government.
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Cruz, I.F., Fabiani, A., Caimi, F., Stroe, C., Palmonari, M. (2012). Automatic Configuration Selection Using Ontology Matching Task Profiling. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds) The Semantic Web: Research and Applications. ESWC 2012. Lecture Notes in Computer Science, vol 7295. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30284-8_19
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DOI: https://doi.org/10.1007/978-3-642-30284-8_19
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