让下一篇重要论文，不再错过。

原文摘要

Temporal Knowledge Graph Question Answering (TKGQA) aims to answer temporal questions using knowledge from Temporal Knowledge Graphs (TKGs).Existing LLM-based TKGQA methods typically utilize RAG-based or Agent-based paradigms, yet both struggle to construct reliable temporal evidence chains. RAG-based approaches primarily rely on semantic retrieval to fetch question-relevant contexts but overlook the structural dependencies within TKGs, leading to broken evidence chains, whereas iterative agents are prone to error propagation during multi-step reasoning.To address these limitations, we propose TECQA, a framework designed to construct temporal evidence chains for LLM reasoning. Firstly, TECQA employs structure-guided subgraph retrieval to capture structural dependencies and intermediate reasoning paths. Subsequently, it utilizes a k-nearest temporal neighbor pruning strategy to filter irrelevant noise while strictly preserving the continuous local history surrounding critical events. Finally, the retained temporal neighbors are serialized by temporal proximity to explicitly reconstruct a coherent temporal evidence chain. Extensive experiments on MultiTQ and CronQuestions demonstrate that TECQA achieves state-of-the-art performance, outperforming strong baselines by 45.3% particularly on complex queries. Code is available at https://github.com/SimonsLiu/TECQA.