A Generalized Spatio-Temporal Causal Knowledge Graph Framework With Large Language Model-Enhanced Reasoning for Urban Event Management

Abstract

Urban event management faces critical challenges in processing unstructured citizen complaints because of the limitations of traditional extraction models in handling colloquial text, static nature of conventional knowledge graphs that overlook spatiotemporal risk propagation, and brittleness of handcrafted symbolic rules that fail to generalize to novel scenarios. To address these issues, this study presents ST-CKG, a generalized spatiotemporal causal knowledge graph framework with Large Language Model (LLM)-enhanced reasoning. The framework integrates three tightly coupled components: a hybrid information extraction module that combines fine-tuned models with an LLM-based verifier to produce confidence-calibrated structured knowledge; a spatiotemporal causal hypergraph construction module that encodes spatial proximity and temporal causal hyperedges to model cascading urban risks; and a neuro-symbolic reasoning engine that distills expert-defined risk causal chain rules into a graph attention network, enabling interpretable and generalizable inference over incomplete or unseen event configurations. Extensive experiments conducted on over 2,000 real-world citizen complaints demonstrated that the hybrid extraction module outperformed standalone baselines by 3-5 percentage points in the F1-score, achieving 91% urgency assessment accuracy, significantly surpassing pure rule-based and pure graph neural network variants in rule-uncovered scenarios. Ablation studies have confirmed the indispensability of each component. The ST-CKG framework offers a replicable paradigm for bridging unstructured civic narratives with structured actionable governance decisions in smart cities.