  Hyper-relational knowledge graphs (HKGs) extend standard knowledge graphs by
associating attribute-value qualifiers to triples, which effectively represent
additional fine-grained information about its associated triple.
Hyper-relational knowledge graph completion (HKGC) aims at inferring unknown
triples while considering its qualifiers. Most existing approaches to HKGC
exploit a global-level graph structure to encode hyper-relational knowledge
into the graph convolution message passing process. However, the addition of
multi-hop information might bring noise into the triple prediction process. To
address this problem, we propose HyperFormer, a model that considers
local-level sequential information, which encodes the content of the entities,
relations and qualifiers of a triple. More precisely, HyperFormer is composed
of three different modules: an entity neighbor aggregator module allowing to
integrate the information of the neighbors of an entity to capture different
perspectives of it; a relation qualifier aggregator module to integrate
hyper-relational knowledge into the corresponding relation to refine the
representation of relational content; a convolution-based bidirectional
interaction module based on a convolutional operation, capturing pairwise
bidirectional interactions of entity-relation, entity-qualifier, and
relation-qualifier. realize the depth perception of the content related to the
current statement. Furthermore, we introduce a Mixture-of-Experts strategy into
the feed-forward layers of HyperFormer to strengthen its representation
capabilities while reducing the amount of model parameters and computation.
Extensive experiments on three well-known datasets with four different
conditions demonstrate HyperFormer's effectiveness. Datasets and code are
available at https://github.com/zhiweihu1103/HKGC-HyperFormer.
