Containment and Complementarity Relationships in Multidimensional Linked Open Data.
Marios Meimaris, George Papastefanatos · 2014
Abstract. The Linked Open Data (LOD) cloud can act as a source of remote multidimensional datasets which are seemingly disparate, but are modeled un-der common directives and thus often share a common meta-model, dimensions and measures, as well as external codelists. This gives them a latent measure of relatedness that is independent of the publishers ’ initial intentions, but a deriva-tive of the motivations behind LOD. In this paper we identify the constituents of relatedness between multidimensional LOD data points (observations) mod-eled with the Data Cube vocabulary, that often exhibit overlapping values both at the schema and at the data level. Treating hierarchies as first-class citizens, we consider observation relatedness in two aspects, namely containment and complementarity, for which we provide formal definitions and representational semantics. Finally, we present a methodology for computing these types of re-latedness and we provide an evaluation over real-world datasets.