Presented at: 9th Extended Semantic Web Conference (ESWC2012)
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 aligned. Using machine learning techniques, we can automatically determine the optimal configuration in most cases. Even using a small training set, our system predicts the best configuration in 94% of the cases. Our approach is evaluated using our extensible and configurable ontology matching system AgreementMaker.
Keywords: automatic configuration selection, machine learning, matching task profiling, ontology alignment, ontology matching, ontology profiling
Resource URI on the dog food server: http://data.semanticweb.org/conference/eswc/2012/paper/research/199
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