Abstract
This paper presents a translation-based knowledge geraph embedding method via efficient relation rotation (TransERR), a straightforward yet effective alternative to traditional translation-based knowledge graph embedding models. Different from the previous translation-based models, TransERR encodes knowledge graphs in the hypercomplex-valued space, thus enabling it to possess a higher degree of translation freedom in mining latent information between the head and tail entities. To further minimize the translation distance, TransERR adaptively rotates the head entity and the tail entity with their corresponding unit quaternions, which are learnable in model training. We also provide mathematical proofs to demonstrate the ability of TransERR in modeling various relation patterns, including symmetry, antisymmetry, inversion, composition, and subrelation patterns. The experiments on 10 benchmark datasets validate the effectiveness and the generalization of TransERR. The results also indicate that TransERR can better encode large-scale datasets with fewer parameters than the previous translation-based models. Our code and datasets are available at https://github.com/dellixx/TransERR.- Anthology ID:
- 2024.lrec-main.1454
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 16727–16737
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.1454
- DOI:
- Cite (ACL):
- Jiang Li, Xiangdong Su, Fujun Zhang, and Guanglai Gao. 2024. TransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 16727–16737, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- TransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotation (Li et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2024.lrec-main.1454.pdf