Abstract
Knowledge graph embedding methods are important for the knowledge graph completion (or link prediction) task.One state-of-the-art method, PairRE, leverages two separate vectors to model complex relations (i.e., 1-to-N, N-to-1, and N-to-N) in knowledge graphs. However, such a method strictly restricts entities on the hyper-ellipsoid surfaces which limits the optimization of entity distribution, leading to suboptimal performance of knowledge graph completion. To address this issue, we propose a novel score function TranSHER, which leverages relation-specific translations between head and tail entities to relax the constraint of hyper-ellipsoid restrictions. By introducing an intuitive and simple relation-specific translation, TranSHER can provide more direct guidance on optimization and capture more semantic characteristics of entities with complex relations. Experimental results show that TranSHER achieves state-of-the-art performance on link prediction and generalizes well to datasets in different domains and scales. Our codes are public available athttps://github.com/yizhilll/TranSHER.- Anthology ID:
- 2022.emnlp-main.583
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8517–8528
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.583
- DOI:
- Cite (ACL):
- Yizhi Li, Wei Fan, Chao Liu, Chenghua Lin, and Jiang Qian. 2022. TranSHER: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal Restriction. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8517–8528, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- TranSHER: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal Restriction (Li et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2022.emnlp-main.583.pdf