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Hyper-relational knowledge graphs (HKGs) extend standard knowledge graphs byassociating attribute-value qualifiers to triples, which effectively representadditional fine-grained information about its associated triple.Hyper-relational knowledge graph completion (HKGC) aims at inferring unknowntriples while considering its qualifiers. Most existing approaches to HKGCexploit a global-level graph structure to encode hyper-relational knowledgeinto the graph convolution message passing process. However, the addition ofmulti-hop information might bring noise into the triple prediction process. Toaddress this problem, we propose HyperFormer, a model that considerslocal-level sequential information, which encodes the content of the entities,relations and qualifiers of a triple. More precisely, HyperFormer is composedof three different modules: an entity neighbor aggregator module allowing tointegrate the information of the neighbors of an entity to capture differentperspectives of it; a relation qualifier aggregator module to integratehyper-relational knowledge into the corresponding relation to refine therepresentation of relational content; a convolution-based bidirectionalinteraction module based on a convolutional operation, capturing pairwisebidirectional interactions of entity-relation, entity-qualifier, andrelation-qualifier. realize the depth perception of the content related to thecurrent statement. Furthermore, we introduce a Mixture-of-Experts strategy intothe feed-forward layers of HyperFormer to strengthen its representationcapabilities while reducing the amount of model parameters and computation.Extensive experiments on three well-known datasets with four differentconditions demonstrate HyperFormer's effectiveness. Datasets and code areavailable at https://github.com/zhiweihu1103/HKGC-HyperFormer.

HyperFormer: Enhancing Entity and Relation Interaction for Hyper-Relational Knowledge Graph Completion