Complex Hyperbolic Knowledge Graph Embeddings with Fast Fourier Transform (2022.emnlp-main)
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| Challenge: | Existing studies have shown that the choice of space for knowledge graph (KG) embeddings has significant effects on the performance of KG completion tasks. |
| Approach: | They propose to use the Fourier transform to convert between real and complex hyperbolic space to capture hierarchical patterns. |
| Outcome: | The proposed models outperform the baseline models for knowledge graph (KG) embeddings. |
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| Challenge: | Recent knowledge graph embedding models based on hyperbolic geometry are complicated than Euclidean operations. |
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| Challenge: | Existing methods for knowledge graph embedding rely on tangent approximation and are not fully hyperbolic. |
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| Challenge: | Existing knowledge graph embedding methods are built on Euclidean space, which are difficult to handle hierarchical structures. |
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| Challenge: | Existing methods for linking knowledge graphs are incomplete and rely on Euclidean embeddings . a hyperbolic GNN framework embeds recursive learning trees in hyperbolical space . |
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| Challenge: | Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embeddable methods. |
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| Challenge: | Existing knowledge graph embeddings rely on geometric operations to model relational patterns such as symmetry and hierarchical semantics. |
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Knowledge Graph Embeddings in Geometric Algebras (2020.coling-main)
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| Challenge: | Existing knowledge graph embedding approaches model entities and relations in KGs using real-valued, complex-value, or hypercomplex-value representations. |
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