Improved Knowledge Base Completion by Path-Augmented TransR Model
  Knowledge base completion aims to infer new relations from existing
information. In this paper, we propose path-augmented TransR (PTransR) model to
improve the accuracy of link prediction. In our approach, we base PTransR model
on TransR, which is the best one-hop model at present. Then we regularize
TransR with information of relation paths. In our experiment, we evaluate
PTransR on the task of entity prediction. Experimental results show that
PTransR outperforms previous models.
