Details

Autor(en) / Beteiligte

• Atencia, Manuel
• David, Jérôme
• Euzenat, Jérôme
• Napoli, Amedeo
• Vizzini, Jérémy

Titel

Link key candidate extraction with relational concept analysis

Ist Teil von

• Discrete Applied Mathematics, 2020-02, Vol.273, p.2-20

Ort / Verlag

Amsterdam: Elsevier B.V

Erscheinungsjahr

2020

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Linked data aims at publishing data expressed in RDF (Resource Description Framework) at the scale of the worldwide web. These datasets interoperate by publishing links which identify individuals across heterogeneous datasets. Such links may be found by using a generalisation of keys in databases, called link keys, which apply across datasets. They specify the pairs of properties to compare for linking individuals belonging to different classes of the datasets. Here, we show how to recast the proposed link key extraction techniques for RDF datasets in the framework of formal concept analysis. We define a formal context, where objects are pairs of resources and attributes are pairs of properties, and show that formal concepts correspond to link key candidates. We extend this characterisation to the full RDF model including non functional properties and interdependent link keys. We show how to use relational concept analysis for dealing with cyclic dependencies across classes and hence link keys. Finally, we discuss an implementation of this framework.