RAG Meets Temporal Graphs: Time-Sensitive Modeling and Retrieval for Evolving Knowledge

• URL: http://arxiv.org/abs/2510.13590v1
• Date: Wed, 15 Oct 2025 14:21:08 GMT
• Title: RAG Meets Temporal Graphs: Time-Sensitive Modeling and Retrieval for Evolving Knowledge
• Authors: Jiale Han, Austin Cheung, Yubai Wei, Zheng Yu, Xusheng Wang, Bing Zhu, Yi Yang,
• Abstract summary: Knowledge is inherently time-sensitive and continuously evolves over time.<n>Current Retrieval-Augmented Generation (RAG) systems largely ignore this temporal nature.<n>We propose Temporal GraphRAG (TG-RAG), which models external corpora as a bi-level temporal graph.
• Score: 12.898085044538433
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge is inherently time-sensitive and continuously evolves over time. Although current Retrieval-Augmented Generation (RAG) systems enrich LLMs with external knowledge, they largely ignore this temporal nature. This raises two challenges for RAG. First, current RAG methods lack effective time-aware representations. Same facts of different time are difficult to distinguish with vector embeddings or conventional knowledge graphs. Second, most RAG evaluations assume a static corpus, leaving a blind spot regarding update costs and retrieval stability as knowledge evolves. To make RAG time-aware, we propose Temporal GraphRAG (TG-RAG), which models external corpora as a bi-level temporal graph consisting of a temporal knowledge graph with timestamped relations and a hierarchical time graph. Multi-granularity temporal summaries are generated for each time node to capture both key events and broader trends at that time. The design supports incremental updates by extracting new temporal facts from the incoming corpus and merging them into the existing graph. The temporal graph explicitly represents identical facts at different times as distinct edges to avoid ambiguity, and the time hierarchy graph allows only generating reports for new leaf time nodes and their ancestors, ensuring effective and efficient updates. During inference, TG-RAG dynamically retrieves a subgraph within the temporal and semantic scope of the query, enabling precise evidence gathering. Moreover, we introduce ECT-QA, a time-sensitive question-answering dataset featuring both specific and abstract queries, along with a comprehensive evaluation protocol designed to assess incremental update capabilities of RAG systems. Extensive experiments show that TG-RAG significantly outperforms existing baselines, demonstrating the effectiveness of our method in handling temporal knowledge and incremental updates.

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