RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion
Hao Huang, Guodong Long, Tao Shen, Jing Jiang, Chengqi Zhang
Abstract
Many graph embedding approaches have been proposed for knowledge graph completion via link prediction. Among those, translating embedding approaches enjoy the advantages of light-weight structure, high efficiency and great interpretability. Especially when extended to complex vector space, they show the capability in handling various relation patterns including symmetry, antisymmetry, inversion and composition. However, previous translating embedding approaches defined in complex vector space suffer from two main issues: 1) representing and modeling capacities of the model are limited by the translation function with rigorous multiplication of two complex numbers; and 2) embedding ambiguity caused by one-to-many relations is not explicitly alleviated. In this paper, we propose a relation-adaptive translation function built upon a novel weighted product in complex space, where the weights are learnable, relation-specific and independent to embedding size. The translation function only requires eight more scalar parameters each relation, but improves expressive power and alleviates embedding ambiguity problem. Based on the function, we then present our Relation-adaptive translating Embedding (RatE) approach to score each graph triple. Moreover, a novel negative sampling method is proposed to utilize both prior knowledge and self-adversarial learning for effective optimization. Experiments verify RatE achieves state-of-the-art performance on four link prediction benchmarks.- Anthology ID:
- 2020.coling-main.48
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 556–567
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.48
- DOI:
- 10.18653/v1/2020.coling-main.48
- Cite (ACL):
- Hao Huang, Guodong Long, Tao Shen, Jing Jiang, and Chengqi Zhang. 2020. RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion. In Proceedings of the 28th International Conference on Computational Linguistics, pages 556–567, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion (Huang et al., COLING 2020)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2020.coling-main.48.pdf
- Data
- FB15k, WN18, WN18RR