Abstract
Reuse and combination of disparate datasets on the Semantic Web require semantic coordination, i.e. the ability to match heterogeneous semantic models. Systematic evaluations raised the performance of matching systems in terms of compliance and resource consumption. However, it is equally important to be able to identify diverse matching scenarios, covering a range of variations in the datasets such as different modeling languages, heterogeneous lexicalizations, structural differences and to be able to properly handle these scenarios through dedicated techniques and the exploitation of external resources. Furthermore, this should be achieved without requiring manual tinkering of low-level configuration knobs. As of the Semantic Web vision, machines should be able to coordinate and talk to each other to solve problems. To that end, we propose a system that automates most decisions by leveraging explicit metadata regarding the datasets to be matched and potentially useful support datasets. This system uses established metadata vocabularies such as VoID, Dublin Core and the LIME module of OntoLex-Lemon. Consequently, the system can work on real-world cases, leveraging metadata already published alongside self-describing datasets.
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Notes
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The expression “ontology matching” is often used in a broader sense than the one the first word of the term would suggest. “Ontology” is in this case a synecdoche for ontologies, thesauri, lexicons and any sort of knowledge resources modeled according to core knowledge modeling languages for the Semantic Web. The expression ontology matching thus defines the task of discovering and assessing alignments between ontologies and other data models of the RDF family; alternative expressions are ontology mapping or ontology alignment. In the RDF jargon, and following the terminology adopted in the VoID metadata vocabulary [29], a set of alignments is also called a Linkset.
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This work has been drafted under the 2016.16 action of the ISA2 Programme (https://ec.europa.eu/isa2).
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Fiorelli, M. et al. (2019). Metadata-Driven Semantic Coordination. In: Garoufallou, E., Fallucchi, F., William De Luca, E. (eds) Metadata and Semantic Research. MTSR 2019. Communications in Computer and Information Science, vol 1057. Springer, Cham. https://doi.org/10.1007/978-3-030-36599-8_2
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