Given a training set S = {(h i , r i , t i ), i = 1, ... , |S|} of relations extracted from the KB, this model learns vector embeddings of the entities and of the relationships using the idea that the functional relation induced by the r -labeled arcs of the KB should correspond to a translation of the embeddings.
given a k-dimensional embedding of the left-hand side (head) entity, adding the k-dimensional embedding of a given relation should yield the point (or close to the point) of the kdimensional embedding of the right-hand side (tail) entity.