In the context of Linked Data, different kinds of semantic links can be established between data. However when data sources are huge, detecting such links manually is not feasible. One of the data linking problems consists of detecting identity links between data expressing that different identifiers refer to the same real world entity. Some automatic data linking approaches use key constraints to infer identity links; nevertheless this kind of knowledge is rarely available.
The objective of this talk is to present KD2R, an approach that allows the automatic discovery of composite key constraints in RDF data sources that may conform to different schemas. We only consider data sources for which the Unique Name Assumption is fulfilled. The obtained keys are correct with respect to the RDF data sources in which they are discovered. During this talk, I will also present experiments that shows that our method scales.
