3D Rotation and Translation for Hyperbolic Knowledge Graph Embedding (2024.eacl-long)
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| Challenge: | Existing knowledge graph embeddings do not capture relation patterns, but they capture symmetry, antisymmetry, inversion, commutative composition, non-commutable composition, hierarchy, and multiplicity. |
| Approach: | They propose a 3D Rotation and Translation in Hyperbolic space model that captures relation patterns simultaneously. |
| Outcome: | The proposed model outperforms state-of-the-art models in terms of accuracy, hierarchy property, and other relation patterns in low-dimensional space, while performing similarly in high-dimensional spaces. |
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