PairRE: Knowledge Graph Embeddings via Paired Relation Vectors

Linlin Chao, Jianshan He, Taifeng Wang, Wei Chu


Abstract
Distance based knowledge graph embedding methods show promising results on link prediction task, on which two topics have been widely studied: one is the ability to handle complex relations, such as N-to-1, 1-to-N and N-to-N, the other is to encode various relation patterns, such as symmetry/antisymmetry. However, the existing methods fail to solve these two problems at the same time, which leads to unsatisfactory results. To mitigate this problem, we propose PairRE, a model with paired vectors for each relation representation. The paired vectors enable an adaptive adjustment of the margin in loss function to fit for different complex relations. Besides, PairRE is capable of encoding three important relation patterns, symmetry/antisymmetry, inverse and composition. Given simple constraints on relation representations, PairRE can encode subrelation further. Experiments on link prediction benchmarks demonstrate the proposed key capabilities of PairRE. Moreover, We set a new state-of-the-art on two knowledge graph datasets of the challenging Open Graph Benchmark.
Anthology ID:
2021.acl-long.336
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Editors:
Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4360–4369
Language:
URL:
https://aclanthology.org/2021.acl-long.336
DOI:
10.18653/v1/2021.acl-long.336
Bibkey:
Cite (ACL):
Linlin Chao, Jianshan He, Taifeng Wang, and Wei Chu. 2021. PairRE: Knowledge Graph Embeddings via Paired Relation Vectors. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4360–4369, Online. Association for Computational Linguistics.
Cite (Informal):
PairRE: Knowledge Graph Embeddings via Paired Relation Vectors (Chao et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.336.pdf
Video:
 https://aclanthology.org/2021.acl-long.336.mp4
Code
 alipay/KnowledgeGraphEmbeddingsViaPairedRelationVectors_PairRE
Data
FB15k-237OGBOpen Graph Benchmark