Temporal knowledge graph reasoning (TKGR) aims to predict future events by inferring missing entities with dynamic knowledge structures. Existing LLM-based reasoning methods prioritize contextual over structural relations, struggling to extract relevant subgraphs from dynamic graphs. This limits structural information understanding, leading to unstructured, hallucination-prone inferences especially with temporal inconsistencies. To address this problem, we propose IGETR (Integration of Graph and Editing-enhanced Temporal Reasoning), a hybrid reasoning framework that combines the structured temporal modeling capabilities of Graph Neural Networks (GNNs) with the contextual understanding of LLMs. IGETR operates through a three-stage pipeline. The first stage aims to ground the reasoning process in the actual data by identifying structurally and temporally coherent candidate paths through a temporal GNN, ensuring that inference starts from reliable graph-based evidence. The second stage introduces LLM-guided path editing to address logical and semantic inconsistencies, leveraging external knowledge to refine and enhance the initial paths. The final stage focuses on integrating the refined reasoning paths to produce predictions that are both accurate and interpretable. Experiments on standard TKG benchmarks show that IGETR achieves state-of-the-art performance, outperforming strong baselines with relative improvements of up to 5.6% on Hits@1 and 8.1% on Hits@3 on the challenging ICEWS datasets. Additionally, we execute ablation studies and additional analyses confirm the effectiveness of each component.

翻译：时序知识图谱推理（TKGR）旨在通过动态知识结构推断缺失实体以预测未来事件。现有基于大语言模型（LLM）的推理方法侧重于上下文关系而非结构关系，难以从动态图中提取相关子图。这限制了对结构信息的理解，导致推理结果缺乏结构且易产生幻觉，尤其在存在时序不一致性时更为突出。为解决该问题，我们提出IGETR（图与编辑增强时序推理集成框架），这是一种混合推理框架，结合了图神经网络（GNN）的结构化时序建模能力与大语言模型的上下文理解能力。IGETR通过三阶段流程运行：第一阶段旨在将推理过程锚定于实际数据，使用时序GNN识别结构及时序一致的候选路径，确保推理始于可靠的图证据基础；第二阶段引入LLM引导的路径编辑，利用外部知识修正初始路径的逻辑与语义不一致性；第三阶段聚焦于整合优化后的推理路径，生成兼具准确性与可解释性的预测结果。在标准TKG基准测试上的实验表明，IGETR取得了最先进的性能，在具有挑战性的ICEWS数据集上，其Hits@1和Hits@3指标分别相对提升最高达5.6%和8.1%，显著优于现有强基线模型。消融研究与补充分析进一步验证了各模块的有效性。