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 the relation prediction task, providing insights into their adaptability and capabilities in complex forecasting scenarios.

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，近期受到广泛关注。虽然时态知识图谱及其泛化形式——超关系时态知识图谱（HTKGs）——为表示简单时态关系提供了直观的结构，但其在高效表达复杂事实方面存在表达能力不足的问题。HTKGs的一个关键局限在于无法支持时态事实中包含两个以上主要实体，而这在现实世界事件中普遍存在。为突破这一限制，本研究探讨了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，证明其具备向后兼容性的同时，能够支持地缘政治事件中常见的两类复杂事实类型。基于此形式化框架，我们构建了基于全球事件数据库POLECAT的htkgh-polecat数据集。最后，我们在关系预测任务上对主流大语言模型进行基准测试与分析，从而揭示其在复杂预测场景中的适应能力与局限性。