Right Answer at the Right Time - Temporal Retrieval-Augmented Generation via Graph Summarization

• URL: http://arxiv.org/abs/2510.16715v1
• Date: Sun, 19 Oct 2025 05:00:04 GMT
• Title: Right Answer at the Right Time - Temporal Retrieval-Augmented Generation via Graph Summarization
• Authors: Zulun Zhu, Haoyu Liu, Mengke He, Siqiang Luo,
• Abstract summary: Question answering in temporal knowledge graphs requires retrieval that is both time-consistent and efficient.<n>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.<n>This design enforces temporal proximity during retrieval, reduces the candidate set of retrieval results, and lowers token consumption without sacrificing accuracy.
• Score: 23.1799368651364
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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