  This paper contributes a novel embedding model which measures the probability
of each belief $\langle h,r,t,m\rangle$ in a large-scale knowledge repository
via simultaneously learning distributed representations for entities ($h$ and
$t$), relations ($r$), and the words in relation mentions ($m$). It facilitates
knowledge completion by means of simple vector operations to discover new
beliefs. Given an imperfect belief, we can not only infer the missing entities,
predict the unknown relations, but also tell the plausibility of the belief,
just leveraging the learnt embeddings of remaining evidences. To demonstrate
the scalability and the effectiveness of our model, we conduct experiments on
several large-scale repositories which contain millions of beliefs from
WordNet, Freebase and NELL, and compare it with other cutting-edge approaches
via competing the performances assessed by the tasks of entity inference,
relation prediction and triplet classification with respective metrics.
Extensive experimental results show that the proposed model outperforms the
state-of-the-arts with significant improvements.
