Recovering uncertain mappings through structural validation and aggregation with the MoTo system
Floriana Esposito, Nicola Fanizzi, Claudia d’Amato · 2010
We present an automated ontology matching methodology, supported by various machine learning techniques, as implemented in the system MoTo. The methodology is two-tiered. On the first stage it uses a meta-learner to elicit certain mappings from those predicted by single matchers induced by a specific base-learner. Then, uncertain mappings are recovered passing through a validation process, followed by the aggregation of the individual predictions through linguistic quantifiers. Experiments on benchmark ontologies demonstrate the effectiveness of the methodology.