Representation Learning with Ordered Relation Paths for Knowledge Graph Completion

Yao Zhu, Hongzhi Liu, Zhonghai Wu, Yang Song, Tao Zhang

Abstract
Incompleteness is a common problem for existing knowledge graphs (KGs), and the completion of KG which aims to predict links between entities is challenging. Most existing KG completion methods only consider the direct relation between nodes and ignore the relation paths which contain useful information for link prediction. Recently, a few methods take relation paths into consideration but pay less attention to the order of relations in paths which is important for reasoning. In addition, these path-based models always ignore nonlinear contributions of path features for link prediction. To solve these problems, we propose a novel KG completion method named OPTransE. Instead of embedding both entities of a relation into the same latent space as in previous methods, we project the head entity and the tail entity of each relation into different spaces to guarantee the order of relations in the path. Meanwhile, we adopt a pooling strategy to extract nonlinear and complex features of different paths to further improve the performance of link prediction. Experimental results on two benchmark datasets show that the proposed model OPTransE performs better than state-of-the-art methods.
Anthology ID:
D19-1268
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2662–2671
Language:
URL:
https://preview.aclanthology.org/remove-affiliations/D19-1268/
DOI:
10.18653/v1/D19-1268
Bibkey:
Cite (ACL):
Yao Zhu, Hongzhi Liu, Zhonghai Wu, Yang Song, and Tao Zhang. 2019. Representation Learning with Ordered Relation Paths for Knowledge Graph Completion. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2662–2671, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Representation Learning with Ordered Relation Paths for Knowledge Graph Completion (Zhu et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/remove-affiliations/D19-1268.pdf
Code
 Peter7Yao/OPTransE
Data
FB15k, WN18
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