  Knowledge graph (KG) embedding aims at learning the latent representations
for entities and relations of a KG in continuous vector spaces. An empirical
observation is that the head (tail) entities connected by the same relation
often share similar semantic attributes -- specifically, they often belong to
the same category -- no matter how far away they are from each other in the KG;
that is, they share global semantic similarities. However, many existing
methods derive KG embeddings based on the local information, which fail to
effectively capture such global semantic similarities among entities. To
address this challenge, we propose a novel approach, which introduces a set of
virtual nodes called \textit{\textbf{relational prototype entities}} to
represent the prototypes of the head and tail entities connected by the same
relations. By enforcing the entities' embeddings close to their associated
prototypes' embeddings, our approach can effectively encourage the global
semantic similarities of entities -- that can be far away in the KG --
connected by the same relation. Experiments on the entity alignment and KG
completion tasks demonstrate that our approach significantly outperforms recent
state-of-the-arts.
