Block-Diagonal Orthogonal Relation and Matrix Entity for Knowledge Graph Embedding (2024.findings-emnlp)
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| Challenge: | Existing knowledge graph embeddings (KGs) are limited in their flexibility and difficulties in generalizing them for higher-dimensional rotations. |
| Approach: | They propose a KGE model employing matrices for entities and block-diagonal orthogonal matrics with Riemannian optimization for relations that captures several relation patterns that rotation-based methods can identify. |
| Outcome: | The proposed model outperforms state-of-the-art models while reducing the number of relation parameters. |
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| Challenge: | Knowledge graph embedding (KGE) is a computational approach to learn continuous vector representations of relations and entities in knowledge graphs. |
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| Challenge: | Existing methods for embedding entities and relations in knowledge graphs are heuristically motivated and theoretical understanding of such embeddables is underdeveloped. |
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| Challenge: | Existing knowledge graphs are incomplete whether they are constructed manually or automatically, limiting the effectiveness when exploited for downstream applications. |
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| Challenge: | Existing knowledge graph embedding models fail to model semantic hierarchies . Existing methods fail to understand the semantic hierarchies of knowledge graphs . |
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Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding (2020.acl-main)
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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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A Mutual Information Perspective on Knowledge Graph Embedding (2025.acl-long)
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Knowledge Graph Embedding with Hierarchical Relation Structure (D18-1)
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| Challenge: | Existing knowledge graph embedding models embed entities and relations into latent vectors without leveraging rich information from relation structure. |
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