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.

翻译：超关系知识图谱（HKG）上的链接预测是一项有价值的研究工作。HKG由超关系事实（H-Fact）构成，每个超关系事实包含一个主三元组和若干辅助属性-值限定符，能够有效表达事实全面的信息。HKG的内部结构在全局上可表示为基于超图的表征，在局部上可表示为基于语义序列的表征。然而，现有研究很少同时建模HKG的图结构和序列结构，这限制了HKG的表征能力。为克服这一局限，我们提出了一种新颖的层次化注意力模型用于HKG嵌入（HAHE），包括全局级注意力和局部级注意力。全局级注意力通过超图双注意力层建模HKG的图结构，而局部级注意力则通过异构自注意力层学习H-Fact内部的序列结构。实验结果表明，HAHE在HKG标准数据集上的链接预测任务中达到了最先进的性能。此外，HAHE首次解决了HKG多位置预测问题，提升了HKG链接预测任务的适用性。我们的代码已公开。