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.

翻译：完成知识基础的目的是从现有信息中推导出新的关系。 在本文中,我们提出了路径增强的TransR(PtransR)模型,以提高链接预测的准确性。 在我们的方法中,我们把PTransR模型以TransR为基础,这是目前最好的一站式模型。然后我们用关系路径的信息来规范TransR。在我们的实验中,我们用实体预测的任务来评估PTransR。实验结果显示PTransR比以前的模型要好。