Finding commonalities in linked open data

The availability of a data source as huge, open, accessible and machine-understandable as the Web of Data asks for new and sophisticated inferences to be implemented in order to deeply exploit such a rich informative content. Towards this direction, the paper proposes an approach for inferring clusters in collections of RDF resources on the basis of the features shared by their descriptions. The approach grounds on an algorithm for Common Subsumers computation proposed in a previous work of some of the authors. The clustering service introduced here returns not only different cluster proposals for a given collection, but also a description of the informative content shared by the RDF resources within the clusters, in terms of (generalized) RDF triples.