Forecasting on geopolitical temporal knowledge graphs (TKGs) through the lens of large language models (LLMs) has recently gained traction. While TKGs and their generalization, hyper-relational temporal knowledge graphs (HTKGs), offer a straightforward structure to represent simple temporal relationships, they lack the expressive power to convey complex facts efficiently. One of the critical limitations of HTKGs is a lack of support for more than two primary entities in temporal facts, which commonly occur in real-world events. To address this limitation, in this work, we study a generalization of HTKGs, Hyper-Relational Temporal Knowledge Generalized Hypergraphs (HTKGHs). We first derive a formalization for HTKGHs, demonstrating their backward compatibility while supporting two complex types of facts commonly found in geopolitical incidents. Then, utilizing this formalization, we introduce the htkgh-polecat dataset, built upon the global event database POLECAT. Finally, we benchmark and analyze popular LLMs on our dataset, providing insights into 1) the positive impact of utilizing the HTKGH formalization compared to existing ones and 2) LLMs' adaptability and capabilities in complex forecasting tasks.

翻译：通过大语言模型（LLMs）的视角对地缘政治时态知识图谱（TKGs）进行预测近来受到关注。虽然时态知识图谱及其泛化形式——超关系时态知识图谱（HTKGs）——提供了一种直接的结构来表示简单的时态关系，但它们缺乏有效传达复杂事实的表达能力。HTKGs的一个关键局限在于不支持时态事实中存在两个以上的主要实体，而这在现实世界事件中普遍存在。为解决这一局限，在本工作中，我们研究了一种HTKGs的泛化形式——超关系时态知识广义超图（HTKGHs）。我们首先推导出HTKGHs的形式化定义，证明其向后兼容性的同时，支持地缘政治事件中常见的两种复杂事实类型。随后，利用此形式化定义，我们引入了基于全球事件数据库POLECAT构建的htkgh-polecat数据集。最后，我们在该数据集上对流行的大语言模型进行了基准测试与分析，从而揭示了：1）相较于现有形式化方法，采用HTKGH形式化带来的积极影响；2）大语言模型在复杂预测任务中的适应性与能力。