Achieving automatic interoperability among systems with diverse data structures and languages expressing different viewpoints is a goal that has been difficult to accomplish. This paper describes S-Match, an open source semantic matching framework that tackles the semantic interoperability problem by transforming several data structures such as business catalogs, web directories, conceptual models and web services descriptions into lightweight ontologies and establishing semantic correspondences between them. The framework is the first open source semantic matching project that includes three different algorithms tailored for specific domains and provides an extensible API for developing new algorithms, including possibility to plug-in specific background knowledge according to the characteristics of each application domain.

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Abstract. Identifying semantic correspondences between different vocabularies has been recognized as a fundamental step towards achieving interoperability. Several manual and automatic techniques have been recently proposed. Fully manual approaches are very precise, but extremely costly. Conversely, automatic approaches tend to fail when domain specific background knowledge is needed. Consequently, they typically require a manual validation step. Yet, when the number of computed correspondences is very large, the validation phase can be very expensive. In order to reduce the problems above, we propose to compute the minimal set of correspondences, that we call the minimal mapping, which are sufficient to compute all the other ones. We show that by concentrating on such correspondences we can save up to 99% of the manual checks required for validation.

Abstract. As a valid solution to the semantic heterogeneity problem, many matching solutions have been proposed. Given two lightweight ontologies, we compute the minimal mapping, namely the subset of all possible correspondences, that we call mapping elements, between them such that i) all the others can be computed from them in time linear in the size of the input ontologies, and ii) none of them can be dropped without losing property i). We provide a formal definition of minimal mappings and define a time efficient computation algorithm which minimizes the number of comparisons between the nodes of the two input ontologies. The experimental results show a substantial improvement both in the computation time and in the number of mapping elements which need to be handled, for instance for validation, navigation and search.