Paper Info
Title
Link key candidate extraction with relational concept analysis
Abstract
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.
Year
DOI
Venue
2020
10.1016/j.dam.2019.02.012
Discrete Applied Mathematics
Keywords
Field
DocType
Formal concept analysis,Relational concept analysis,Linked data,Link key,Data interlinking,Resource description framework
Interdependence,Discrete mathematics,Non functional,Information retrieval,Interoperability,Generalization,Linked data,Relational concept analysis,Formal concept analysis,Mathematics,RDF
Journal
Volume
ISSN
Citations 
273
0166-218X
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Manuel Atencia18810.79
Jérôme David222018.27
Jerôme Euzenat3978.91
Amedeo Napoli41180135.52
Jérémy Vizzini500.34