Toward Better Temporal Structures for Geopolitical Events Forecasting

2026-01-01

arXiv.org

AI Summary

Existing hyper-relational temporal knowledge graphs (HTKGs) struggle to model complex geopolitical events involving multiple head entities, limiting their expressiveness and predictive performance. This work proposes Hyper-relational Temporal Knowledge Generalized Hypergraphs (HTKGHs), which introduce generalized hypergraphs into temporal knowledge representation for the first time, enabling the modeling of complex events with multiple head entities while maintaining backward compatibility with conventional HTKGs. Leveraging the POLECAT global event database, we construct htkgh-polecat—the first dataset tailored to this structure—and conduct a systematic evaluation of mainstream large language models on complex temporal relation prediction tasks. Our analysis reveals both the potential and limitations of these models in forecasting multi-entity geopolitical events.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• temporal knowledge graphs
• hyper-relational
• geopolitical events
• complex facts
• multi-entity relations

Innovation

• Hyper-Relational Temporal Knowledge Graphs
• Generalized Hypergraphs
• Geopolitical Event Forecasting
• Multi-entity Temporal Facts
• LLM Benchmarking