  Different from traditional knowledge graphs (KGs) where facts are represented
as entity-relation-entity triplets, hyper-relational KGs (HKGs) allow triplets
to be associated with additional relation-entity pairs (a.k.a qualifiers) to
convey more complex information. How to effectively and efficiently model the
triplet-qualifier relationship for prediction tasks such as HKG completion is
an open challenge for research. This paper proposes to improve the
best-performing method in HKG completion, namely STARE, by introducing two
novel revisions: (1) Replacing the computation-heavy graph neural network
module with light-weight entity/relation embedding processing techniques for
efficiency improvement without sacrificing effectiveness; (2) Adding a
qualifier-oriented auxiliary training task for boosting the prediction power of
our approach on HKG completion. The proposed approach consistently outperforms
STARE in our experiments on three benchmark datasets, with significantly
improved computational efficiency.
