Improved Knowledge Base Completion by Path-Augmented TransR Model
This work addresses knowledge base completion for AI systems, but it appears incremental as it builds on an existing method.
The paper tackled knowledge base completion by proposing a path-augmented TransR model to improve link prediction accuracy, and experimental results showed it outperformed previous models.
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.