让AI理解知识随时间变化，解决检索中的时间冲突与重复问题。
T-GRAG: A Dynamic GraphRAG Framework for Resolving Temporal Conflicts and Redundancy in Knowledge Retrieval
- 构建动态时间知识图谱，追踪知识演化过程
- 在时间约束下实现更精准的检索与回答，准确率显著提升
- 适合需要时序推理的金融、法律等长文本问答场景
大型语言模型在自然语言生成方面表现强劲，但在依赖知识的任务中受限于内部知识过时或不完整。检索增强生成（RAG）通过引入外部检索缓解此问题，而图RAG进一步利用结构化知识图谱和多跳推理提升性能。然而现有图RAG方法普遍忽略知识的时间动态性，导致时间模糊、时间无关检索和语义冗余等问题。为此，我们提出时序图RAG（T-GRAG），一种动态、具备时间感知能力的RAG框架，用于建模知识随时间的演变。T-GRAG包含五个核心组件：（1）时间知识图谱生成器，创建带时间戳的动态图结构；（2）时序查询分解机制，将复杂时序查询拆分为可处理的子查询；（3）三层交互式检索器，逐层过滤并精炼跨时序子图的检索结果；（4）源文本提取器以降低噪声；（5）基于LLM的生成器，合成时空一致的回答。我们还构建了基于真实企业年报的新型基准数据集Time-LongQA，用于测试跨时间演化的时序推理能力。大量实验表明，T-GRAG在时间约束下显著优于现有RAG与图RAG基线，在检索准确率和回答相关性上均有大幅提升，凸显建模知识演进对鲁棒长文本问答的重要性。代码已公开。
原文摘要 · Abstract (English)
Large language models (LLMs) have demonstrated strong performance in natural language generation but remain limited in knowle- dge-intensive tasks due to outdated or incomplete internal knowledge. Retrieval-Augmented Generation (RAG) addresses this by incorporating external retrieval, with GraphRAG further enhancing performance through structured knowledge graphs and multi-hop reasoning. However, existing GraphRAG methods largely ignore the temporal dynamics of knowledge, leading to issues such as temporal ambiguity, time-insensitive retrieval, and semantic redundancy. To overcome these limitations, we propose Temporal GraphRAG (T-GRAG), a dynamic, temporally-aware RAG framework that models the evolution of knowledge over time. T-GRAG consists of five key components: (1) a Temporal Knowledge Graph Generator that creates time-stamped, evolving graph structures; (2) a Temporal Query Decomposition mechanism that breaks complex temporal queries into manageable sub-queries; (3) a Three-layer Interactive Retriever that progressively filters and refines retrieval across temporal subgraphs; (4) a Source Text Extractor to mitigate noise; and (5) a LLM-based Generator that synthesizes contextually and temporally accurate responses. We also introduce Time-LongQA, a novel benchmark dataset based on real-world corporate annual reports, designed to test temporal reasoning across evolving knowledge. Extensive experiments show that T-GRAG significantly outperforms prior RAG and GraphRAG baselines in both retrieval accuracy and response relevance under temporal constraints, highlighting the necessity of modeling knowledge evolution for robust long-text question answering. Our code is publicly available on the T-GRAG
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