Question answering in temporal knowledge graphs requires retrieval that is both time-consistent and efficient. Existing RAG methods are largely semantic and typically neglect explicit temporal constraints, which leads to time-inconsistent answers and inflated token usage. We propose STAR-RAG, a temporal GraphRAG framework that relies on two key ideas: building a time-aligned rule graph and conducting propagation on this graph to narrow the search space and prioritize semantically relevant, time-consistent evidence. This design enforces temporal proximity during retrieval, reduces the candidate set of retrieval results, and lowers token consumption without sacrificing accuracy. Compared with existing temporal RAG approaches, STAR-RAG eliminates the need for heavy model training and fine-tuning, thereby reducing computational cost and significantly simplifying deployment.Extensive experiments on real-world temporal KG datasets show that our method achieves improved answer accuracy while consuming fewer tokens than strong GraphRAG baselines.

翻译：时间知识图谱中的问答任务需要检索过程同时满足时间一致性与效率要求。现有RAG方法主要基于语义层面，通常忽略显式的时间约束，导致答案存在时间不一致性且令牌消耗量过大。我们提出STAR-RAG，一种时序GraphRAG框架，其核心基于两个关键思想：构建时间对齐的规则图，并在此图上进行传播推理以缩小搜索空间，优先获取语义相关且时间一致的证据。该设计在检索过程中强制实施时间邻近性约束，缩减检索结果的候选集，在不牺牲准确性的前提下降低令牌消耗。与现有时序RAG方法相比，STAR-RAG无需繁重的模型训练与微调过程，从而降低计算成本并显著简化部署流程。在真实世界时序知识图谱数据集上的大量实验表明，相较于强大的GraphRAG基线方法，我们的方法在消耗更少令牌的同时实现了更高的答案准确率。