Recovering uncertain mappings through structural validation and aggregation with the MoTo system

Authors:
Floriana Esposito;Nicola Fanizzi;Claudia d'Amato
Affiliations:
University of Bari, Italy;University of Bari, Italy;University of Bari, Italy
Venue:
Proceedings of the 2010 ACM Symposium on Applied Computing
Year:
2010

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Abstract

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.