Abstract. Multidimensional data are published in the web of data under common directives, such as the Resource Description Framework (RDF). The increasing volume and diversity of these data pose the challenge of finding relations between them in a most efficient and accurate way, by taking into advantage their overlapping schemes. In this paper we define two types of relationships between multidimensional RDF data, and we propose algorithms for efficient and scalable computation of these relationships. Specifically, we define the notions of containment and complementarity between points in multidimensional dataspaces, as different aspects of relatedness, and we propose a baseline method for computing them, as well as two alternative methods that target speed and scalability. We provide an experimental evaluation over real-world and synthetic datasets and we compare our approach to a SPARQL-based and a rule-based alternative, which prove to be inefficient for increasing input sizes.