Abstract
The main objective of Knowledge Graph (KG) embeddings is to learn low-dimensional representations of entities and relations, enabling the prediction of missing facts. A significant challenge in achieving better KG embeddings lies in capturing relation patterns, including symmetry, antisymmetry, inversion, commutative composition, non-commutative composition, hierarchy, and multiplicity. This study introduces a novel model called 3H-TH (3D Rotation and Translation in Hyperbolic space) that captures these relation patterns simultaneously. In contrast, previous attempts have not achieved satisfactory performance across all the mentioned properties at the same time. The experimental results demonstrate that the new model outperforms existing state-of-the-art models in terms of accuracy, hierarchy property, and other relation patterns in low-dimensional space, meanwhile performing similarly in high-dimensional space.- Anthology ID:
- 2024.eacl-long.90
- Volume:
- Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- March
- Year:
- 2024
- Address:
- St. Julian’s, Malta
- Editors:
- Yvette Graham, Matthew Purver
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1497–1515
- Language:
- URL:
- https://aclanthology.org/2024.eacl-long.90
- DOI:
- Cite (ACL):
- Yihua Zhu and Hidetoshi Shimodaira. 2024. 3D Rotation and Translation for Hyperbolic Knowledge Graph Embedding. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1497–1515, St. Julian’s, Malta. Association for Computational Linguistics.
- Cite (Informal):
- 3D Rotation and Translation for Hyperbolic Knowledge Graph Embedding (Zhu & Shimodaira, EACL 2024)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2024.eacl-long.90.pdf