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- W2949885492 abstract "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." @default.
- W2949885492 created "2019-06-27" @default.
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- W2949885492 date "2016-10-06" @default.
- W2949885492 modified "2023-09-23" @default.
- W2949885492 title "Improved Knowledge Base Completion by Path-Augmented TransR Model" @default.
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- W2949885492 doi "https://doi.org/10.48550/arxiv.1610.04073" @default.
- W2949885492 hasPublicationYear "2016" @default.
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