Leveraging Implicit Contexts via LLM–Graph Fusion for Temporal Knowledge Graph Reasoning

Abstract

Temporal knowledge graph (TKG) reasoning is critical for modeling and forecasting the evolution of real-world events. Existing TKG construction pipelines transform raw text into structured temporal quadruples as graph facts. However, in this process, they often fail to preserve reasoning-relevant contextual semantics from the original corpus that cannot be explicitly represented as graph facts, leaving such implicit contextual information unused by current temporal reasoning models. To address this limitation, we propose a Textual-Temporal Graph Fusion Network (TTGFN), a context-aware framework that leverages implicit contexts from text via LLM-based semantic encoding and fuses it with structure-constrained temporal graph representations for reasoning. To the best of our knowledge, this is the first work to systematically leverage LLMs to reuse previously overlooked implicit contextual information and incorporate it into temporal knowledge graph reasoning, substantially improving model performance. Extensive experiments conducted on three TKG reasoning benchmark datasets demonstrate that TTGFN outperforms the state-of-the-art approaches, with Hits@1 gains of 20.85% on ICEWS14 dataset, 31.14% on ICEWS05-15 dataset, and 24.22% on ICEWS18, respectively.