Hyperbolic Hypergraph Neural Networks for Multi-Relational Knowledge Hypergraph Representation
This addresses the challenge of representing multi-relational knowledge hypergraphs for AI applications, offering a novel method to improve accuracy in tasks like link prediction.
The paper tackled the problem of knowledge hypergraph representation by proposing Hyperbolic Hypergraph Neural Networks (H2GNN), which outperformed 15 baselines in node classification and link prediction tasks.
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.