Learning Higher-Order Features for Relation Prediction in Knowledge Hypergraph

Knowledge Hypergraph (KHG) is a higher -order extension of the Knowledge Graph (KG), and its relation prediction is based on known data to predict unknown higher -order relations, thereby providing useful knowledge services. However, the existing KHG relation algorithms still have some limitations: (i) most studies only consider the influence of the direct neighbors, and (ii) they ignore the complex interactions existing inside higher -order facts. Based on this, we propose a KHG relation prediction model HoGCNF2 based on higher -order hypergraph convolutional network and feature fusion. Dual -channel hypergraph convolutional network considers the significant and higher -order information propagation of entities. Feature fusion strategy considers different types of higher -order structures. Besides, attention mechanism adaptively assigns weights to the learned embeddings. Extensive experiments demonstrate the superiority of HoGCNF2 on different datasets. Specifically, the MRR result improves by 2.6% on the unfixed dataset FB-AUTO, and improves by 9.7% on the fixed dataset WikiPeople-4. Our implementations are publicly available at: https://doi.org/10.24433/CO. 5584354.v1.