Containment and Complementarity Relationships in Multidimensional Linked Open Data

The Linked Open Data (LOD) cloud can act as a source of remote multidimensional datasets which are seemingly disparate, but are modeled under 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 derivative of the motivations behind LOD. In this paper we identify the constituents of relatedness between multidimensional LOD data points (observations) modeled 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 relatedness and we provide an evaluation over real-world datasets.