This paper introduces a method and a tool for automatically aligning OWL ontologies, a crucial step for achieving the interoperability of heterogeneous systems in the Semantic Web. Different components are combined for finding suitable mapping candidates (together with their weights), and the set of rules with maximum matching probability is selected. Machine learning-based classifiers and a new classifier using the structure and the semantics of the OWL ontologies are proposed. Our method has been implemented and evaluated on an independent test set provided by the international ontology alignment contest EON. We provide the results of this evaluation with respect to the other competitors.
oMAP: Results of the Ontology Alignment Contest
Straccia U;
2005
Abstract
This paper introduces a method and a tool for automatically aligning OWL ontologies, a crucial step for achieving the interoperability of heterogeneous systems in the Semantic Web. Different components are combined for finding suitable mapping candidates (together with their weights), and the set of rules with maximum matching probability is selected. Machine learning-based classifiers and a new classifier using the structure and the semantics of the OWL ontologies are proposed. Our method has been implemented and evaluated on an independent test set provided by the international ontology alignment contest EON. We provide the results of this evaluation with respect to the other competitors.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
