In the field of representation learning on knowledge graphs (KGs), a hyper-relational fact consists of a main triple and several auxiliary attribute-value descriptions, which is considered more comprehensive and specific than a triple-based fact. However, currently available hyper-relational KG embedding methods in a single view are limited in application because they weaken the hierarchical structure that represents the affiliation between entities. To overcome this limitation, we propose a dual-view hyper-relational KG structure (DH-KG) that contains a hyper-relational instance view for entities and a hyper-relational ontology view for concepts that are abstracted hierarchically from the entities. This paper defines link prediction and entity typing tasks on DH-KG for the first time and constructs two DH-KG datasets, JW44K-6K, extracted from Wikidata, and HTDM based on medical data. Furthermore, we propose DHGE, a DH-KG embedding model based on GRAN encoders, HGNNs, and joint learning. DHGE outperforms baseline models on DH-KG, according to experimental results. Finally, we provide an example of how this technology can be used to treat hypertension. Our model and new datasets are publicly available.

翻译：摘要：在知识图谱表示学习领域，超关系事实由一个主三元组及若干辅助属性-值描述构成，相较于基于三元组的事实，被认为更具全面性与特异性。然而，现有单视角下的超关系知识图谱嵌入方法因弱化实体间隶属关系的层次结构，其应用受到局限。为克服此限制，本文提出一种双重视角超关系知识图谱结构（DH-KG），该结构包含面向实体的超关系实例视图与面向从实体层次化抽象得到的概念的超关系本体视图。本文首次定义了DH-KG上的链接预测与实体类型任务，并构建了两个DH-KG数据集：基于Wikidata提取的JW44K-6K，以及基于医学数据的HTDM。此外，我们提出了DHGE——一种基于GRAN编码器、HGNN与联合学习的DH-KG嵌入模型。实验结果表明，DHGE在DH-KG上优于基线模型。最后，我们给出了该技术在高血压治疗中的应用示例。我们的模型与新型数据集均已公开。