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Link Prediction on Hyper-relational Knowledge Graphs (HKG) is a worthwhile
endeavor. HKG consists of hyper-relational facts (H-Facts), composed of a main
triple and several auxiliary attribute-value qualifiers, which can effectively
represent factually comprehensive information. The internal structure of HKG
can be represented as a hypergraph-based representation globally and a semantic
sequence-based representation locally. However, existing research seldom
simultaneously models the graphical and sequential structure of HKGs, limiting
HKGs' representation. To overcome this limitation, we propose a novel
Hierarchical Attention model for HKG Embedding (HAHE), including global-level
and local-level attention. The global-level attention can model the graphical
structure of HKG using hypergraph dual-attention layers, while the local-level
attention can learn the sequential structure inside H-Facts via heterogeneous
self-attention layers. Experiment results indicate that HAHE achieves
state-of-the-art performance in link prediction tasks on HKG standard datasets.
In addition, HAHE addresses the issue of HKG multi-position prediction for the
first time, increasing the applicability of the HKG link prediction task. Our
code is publicly available.
