用超图结构提升地缘事件预测的复杂事实表达能力
Toward Better Temporal Structures for Geopolitical Events Forecasting
- 提出HTKGH超图模型，支持多实体复杂事件建模
- 构建POLECAT数据集，验证新结构在预测中的优势
- 揭示大模型对复杂时序事件的适应性与潜力
通过大语言模型（LLMs）对地缘政治时序知识图谱（TKGs）进行预测正成为研究热点。尽管TKGs及其推广形式超关系时序知识图谱（HTKGs）能简洁表示简单时序关系，但难以高效表达复杂事实。现有HTKGs无法支持超过两个主体的时序事实，而这在现实事件中很常见。为此，本文研究了HTKGs的推广形式——超关系时序知识广义超图（HTKGH）。首先，我们形式化定义了HTKGH，证明其向后兼容性，并支持两类地缘事件中常见的复杂事实类型。基于此形式化，我们构建了htkgh-polecat数据集，基于全球事件数据库POLECAT。最后，我们在该数据集上基准测试并分析主流大模型，揭示：1）相比现有方法，采用HTKGH形式化可显著提升性能；2）大模型在复杂预测任务中具备良好适应性与表现能力。
原文摘要 · Abstract (English)
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.
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