Summary of Fully Hyperbolic Rotation For Knowledge Graph Embedding, by Qiuyu Liang and Weihua Wang and Feilong Bao and Guanglai Gao
Fully Hyperbolic Rotation for Knowledge Graph Embedding
by Qiuyu Liang, Weihua Wang, Feilong Bao, Guanglai Gao
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel fully hyperbolic model for knowledge graph embedding improves upon existing models by directly defining the model in hyperbolic space with the Lorentz model. This approach considers each relation as a Lorentz rotation from the head entity to the tail entity, using the Lorentzian version distance as the scoring function. The model achieves competitive results on standard benchmarks and state-of-the-art performance on more diverse datasets like CoDEx-s and CoDEx-m. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Knowledge graph embedding is crucial for modeling knowledge graphs and their hierarchies. A new approach to this problem uses a fully hyperbolic model, directly defining the model in hyperbolic space with the Lorentz model. This helps to better understand relationships between entities and improves performance on tasks like triplet prediction. |
Keywords
» Artificial intelligence » Embedding » Knowledge graph
