There has been an increasing interest in studying graph reasoning over hyper-relational KGs (HKGs). Compared with traditional knowledge graphs (KGs), HKGs introduce additional factual information in the form of qualifiers (key-value pairs) for each KG fact that helps to better restrict the fact validity. Meanwhile, due to the ever-evolving nature of world knowledge, extensive parallel works have been studying temporal KG (TKG) reasoning. Each TKG fact can be viewed as a KG fact coupled with a timestamp (or time period) specifying its time validity. The existing HKG reasoning approaches do not consider temporal information because it is not explicitly specified in previous benchmark datasets. Besides, traditional TKG reasoning methods only focus on temporal reasoning and have no way to learn from qualifiers. To this end, we aim to fill the gap between TKG and HKG reasoning. We develop two new benchmark hyper-relational TKG (HTKG) datasets, i.e., Wiki-hy and YAGO-hy, and propose an HTKG reasoning model that efficiently models both temporal facts and qualifiers. We further exploit additional time-invariant relational knowledge from the Wikidata knowledge base to improve HTKG reasoning. Time-invariant relational knowledge serves as the knowledge that remains unchanged in time (e.g., Sasha Obama is the child of Barack Obama). Experimental results show that our model achieves strong performance on HTKG link prediction and can be enhanced by jointly leveraging both temporal and time-invariant relational knowledge.

翻译：近年来，对超关系知识图谱（HKG）上的图推理研究日益增多。与传统知识图谱（KG）相比，HKG以限定符（键值对）的形式为每条KG事实引入额外的事实信息，有助于更严格地约束事实的有效性。同时，由于世界知识不断演化的特性，大量并行工作正在研究时序知识图谱（TKG）推理。每条TKG事实可被视为一个KG事实与一个指定其时间有效性的时间戳（或时间周期）的耦合。现有HKG推理方法未考虑时序信息，因为先前基准数据集中并未明确标注这一信息。此外，传统TKG推理方法仅聚焦于时序推理，无法从限定符中学习。为此，我们旨在弥合TKG与HKG推理之间的鸿沟。我们构建了两个新的基准超关系TKG（HTKG）数据集，即Wiki-hy和YAGO-hy，并提出一种能高效建模时序事实和限定符的HTKG推理模型。我们进一步利用来自Wikidata知识库的额外时间不变关系知识来改进HTKG推理。时间不变关系知识指随时间保持不变的知识（例如，萨莎·奥巴马是巴拉克·奥巴马的孩子）。实验结果表明，我们的模型在HTKG链接预测任务上表现出色，并且通过联合利用时序和不变关系知识可得到进一步增强。