让问答更准更快：时间对齐图检索增强生成
Right Answer at the Right Time - Temporal Retrieval-Augmented Generation via Graph Summarization
- 构建时间对齐规则图，通过传播缩小搜索范围
- 答案准确率更高，令牌消耗减少30%以上
- 无需训练，适合快速部署的时序知识问答
时序知识图谱中的问答需要兼顾时间一致性与高效检索。现有RAG方法多依赖语义匹配，常忽略显式时间约束，导致答案时间不一致且令牌使用量过高。本文提出STAR-RAG，基于两大核心思想：构建时间对齐规则图，并在该图上进行传播以缩小候选集、优先选择语义相关且时间一致的证据。该设计在检索中强制时间邻近性，减少候选结果数量，降低令牌消耗，同时保持高准确性。相比现有时序RAG方法，STAR-RAG无需大量模型训练与微调，显著降低计算成本并简化部署。在真实世界时序知识图谱数据集上的实验表明，该方法在提升答案准确率的同时，令牌消耗低于强基线GraphRAG模型。
原文摘要 · Abstract (English)
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.
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