Collective entity linking is a core natural language processing task, which consists in jointly identifying the entities of a knowledge base (KB) that are mentioned in a text exploiting existing relations between entities within the KB. State-of-the-art methods typically combine local scores accounting for the similarity between mentions and entities, with a global score measuring the coherence of the set of selected entities. The latter relies on the structure of a KB: the hyperlink graph of Wikipedia in most cases or the graph of an RDF KB, e.g., BaseKB or Yago, to benefit from the precise semantics of relationships between entities. In this paper, we devise a novel RDF-based entity relatedness measure for global scores with important properties: (i) it has a clear semantics, (ii) it can be calculated at reasonable computational cost, and (iii) it accounts for the transitive aspects of entity relatedness through existing (bounded length) property paths between entities in an RDF KB. Further, we experimentally show on the TAC-KBP2017 dataset, both with BaseKB and Yago, that it provides significant improvement over state-of-the-art entity relatedness measures for the collective entity linking task.