Knowledge graphs encode semantics that describes resources in terms of several aspects, e.g., neighbors, class hierarchies, or node degrees. Assessing relatedness of knowledge graph entities is crucial for several data-driven tasks, e.g., ranking, clustering, or link discovery.
When linked data applications communicate, they commonly use messaging technologies in which the message exchange itself is not represented as linked data, since it takes place on a different architectural level.
