We propose an approach for inferring clusters in collections of RDF resources based on the features shared by their descriptions. The approach grounds on an algorithm for computing Common Subsumers in RDF proposed in a previous research work. The clustering service introduced here returns not only a possible partition of resources in a collection, but also a description of the knowledge shared within each cluster, in terms of (generalized) RDF triples.

A deductive approach to the identification and description of clusters in Linked Open Data / Colucci, Simona; Giannini, Silvia; Donini, Francesco M.; DI SCIASCIO, Eugenio. - 263:(2014), pp. 987-988. (Intervento presentato al convegno 21st European Conference on Artificial Intelligence, ECAI 2014 tenutosi a Prague, Czech Republic nel August 18-22, 2014) [10.3233/978-1-61499-419-0-987].

A deductive approach to the identification and description of clusters in Linked Open Data

COLUCCI, Simona;GIANNINI, Silvia;DI SCIASCIO, Eugenio
2014-01-01

Abstract

We propose an approach for inferring clusters in collections of RDF resources based on the features shared by their descriptions. The approach grounds on an algorithm for computing Common Subsumers in RDF proposed in a previous research work. The clustering service introduced here returns not only a possible partition of resources in a collection, but also a description of the knowledge shared within each cluster, in terms of (generalized) RDF triples.
2014
21st European Conference on Artificial Intelligence, ECAI 2014
978-1-61499-418-3
A deductive approach to the identification and description of clusters in Linked Open Data / Colucci, Simona; Giannini, Silvia; Donini, Francesco M.; DI SCIASCIO, Eugenio. - 263:(2014), pp. 987-988. (Intervento presentato al convegno 21st European Conference on Artificial Intelligence, ECAI 2014 tenutosi a Prague, Czech Republic nel August 18-22, 2014) [10.3233/978-1-61499-419-0-987].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/103801
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