Bridging Graph Structure and Knowledge-Guided Editing for Interpretable Temporal Knowledge Graph Reasoning
Shiqi Fan, Quanming Yao, Hongyi Nie, Wentao Ma, Zhen Wang, Wen Hua

TL;DR
This paper introduces IGETR, a hybrid framework combining GNNs and LLMs for interpretable and accurate temporal knowledge graph reasoning, addressing structural and semantic challenges in dynamic data.
Contribution
IGETR is a novel three-stage reasoning framework that integrates graph-based evidence, LLM-guided path editing, and path integration for improved temporal knowledge graph inference.
Findings
Achieves state-of-the-art results on ICEWS datasets.
Outperforms baselines with up to 5.6% improvement in Hits@1.
Ablation studies validate each component's effectiveness.
Abstract
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…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Explainable Artificial Intelligence (XAI) · Machine Learning in Healthcare
