Hyperbolic Hypergraph Neural Networks for Multi-Relational Knowledge Hypergraph Representation
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929633919762432 |
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| author | Li, Mengfan Shi, Xuanhua Qiao, Chenqi Zhang, Teng Jin, Hai |
| author_facet | Li, Mengfan Shi, Xuanhua Qiao, Chenqi Zhang, Teng Jin, Hai |
| contents | Knowledge hypergraphs generalize knowledge graphs using hyperedges to connect multiple entities and depict complicated relations. Existing methods either transform hyperedges into an easier-to-handle set of binary relations or view hyperedges as isolated and ignore their adjacencies. Both approaches have information loss and may potentially lead to the creation of sub-optimal models. To fix these issues, we propose the Hyperbolic Hypergraph Neural Network (H2GNN), whose essential component is the hyper-star message passing, a novel scheme motivated by a lossless expansion of hyperedges into hierarchies. It implements a direct embedding that consciously incorporates adjacent entities, hyper-relations, and entity position-aware information. As the name suggests, H2GNN operates in the hyperbolic space, which is more adept at capturing the tree-like hierarchy. We compare H2GNN with 15 baselines on knowledge hypergraphs, and it outperforms state-of-the-art approaches in both node classification and link prediction tasks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_12158 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hyperbolic Hypergraph Neural Networks for Multi-Relational Knowledge Hypergraph Representation Li, Mengfan Shi, Xuanhua Qiao, Chenqi Zhang, Teng Jin, Hai Machine Learning Artificial Intelligence Knowledge hypergraphs generalize knowledge graphs using hyperedges to connect multiple entities and depict complicated relations. Existing methods either transform hyperedges into an easier-to-handle set of binary relations or view hyperedges as isolated and ignore their adjacencies. Both approaches have information loss and may potentially lead to the creation of sub-optimal models. To fix these issues, we propose the Hyperbolic Hypergraph Neural Network (H2GNN), whose essential component is the hyper-star message passing, a novel scheme motivated by a lossless expansion of hyperedges into hierarchies. It implements a direct embedding that consciously incorporates adjacent entities, hyper-relations, and entity position-aware information. As the name suggests, H2GNN operates in the hyperbolic space, which is more adept at capturing the tree-like hierarchy. We compare H2GNN with 15 baselines on knowledge hypergraphs, and it outperforms state-of-the-art approaches in both node classification and link prediction tasks. |
| title | Hyperbolic Hypergraph Neural Networks for Multi-Relational Knowledge Hypergraph Representation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2412.12158 |