T-GRAG: A Dynamic GraphRAG Framework for Resolving Temporal Conflicts and Redundancy in Knowledge Retrieval

• URL: http://arxiv.org/abs/2508.01680v1
• Date: Sun, 03 Aug 2025 09:15:36 GMT
• Title: T-GRAG: A Dynamic GraphRAG Framework for Resolving Temporal Conflicts and Redundancy in Knowledge Retrieval
• Authors: Dong Li, Yichen Niu, Ying Ai, Xiang Zou, Biqing Qi, Jianxing Liu,
• Abstract summary: We propose Temporal GraphRAG (T-GRAG), a dynamic, temporally-aware RAG framework that models the evolution of knowledge over time.<n>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; and (3) a Three-layer Interactive Retriever that progressively filters and refines retrieval across temporal subgraphs.<n>Extensive experiments show that T-GRAG significantly outperforms prior RAG and GraphRAG baselines in both retrieval accuracy and response
• Score: 4.114480531154174
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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demonstrated strong performance in natural language generation but remain limited in knowle- dge-intensive tasks due to outdated or incomplete internal knowledge.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

Related papers

• DyG-RAG: Dynamic Graph Retrieval-Augmented Generation with Event-Centric Reasoning [38.28580037356542]
  We introduce DyG-RAG, a novel event-centric dynamic graph retrieval-augmented generation framework.<n>To capture and reason over temporal knowledge embedded in unstructured text, DyG-RAG proposes Dynamic Event Units (DEUs)<n>To ensure temporally consistent generation, DyG-RAG introduces an event timeline retrieval pipeline.
  arXiv  Detail & Related papers  (2025-07-16T10:22:35Z)
• T-GRAB: A Synthetic Diagnostic Benchmark for Learning on Temporal Graphs [6.199165061105655]
  We introduce the Temporal Graph Reasoning Benchmark (T-GRAB) to systematically probe the capabilities of TGNNs to reason across time.<n>T-GRAB provides controlled, interpretable tasks that isolate key temporal skills.<n>We evaluate 11 temporal graph learning methods on these tasks, revealing fundamental shortcomings in their ability to generalize temporal patterns.
  arXiv  Detail & Related papers  (2025-07-14T11:47:43Z)
• Respecting Temporal-Causal Consistency: Entity-Event Knowledge Graphs for Retrieval-Augmented Generation [69.45495166424642]
  We develop a robust and discriminative QA benchmark to measure temporal, causal, and character consistency understanding in narrative documents.<n>We then introduce Entity-Event RAG (E2RAG), a dual-graph framework that keeps separate entity and event subgraphs linked by a bipartite mapping.<n>Across ChronoQA, our approach outperforms state-of-the-art unstructured and KG-based RAG baselines, with notable gains on causal and character consistency queries.
  arXiv  Detail & Related papers  (2025-06-06T10:07:21Z)
• Align-GRAG: Reasoning-Guided Dual Alignment for Graph Retrieval-Augmented Generation [75.9865035064794]
  Large language models (LLMs) have demonstrated remarkable capabilities, but still struggle with issues like hallucinations and outdated information.<n>Retrieval-augmented generation (RAG) addresses these issues by grounding LLM outputs in external knowledge with an Information Retrieval (IR) system.<n>We propose Align-GRAG, a novel reasoning-guided dual alignment framework in post-retrieval phrase.
  arXiv  Detail & Related papers  (2025-05-22T05:15:27Z)
• RGL: A Graph-Centric, Modular Framework for Efficient Retrieval-Augmented Generation on Graphs [58.10503898336799]
  We introduce the RAG-on-Graphs Library (RGL), a modular framework that seamlessly integrates the complete RAG pipeline.<n>RGL addresses key challenges by supporting a variety of graph formats and integrating optimized implementations for essential components.<n>Our evaluations demonstrate that RGL not only accelerates the prototyping process but also enhances the performance and applicability of graph-based RAG systems.
  arXiv  Detail & Related papers  (2025-03-25T03:21:48Z)
• Beyond Single Pass, Looping Through Time: KG-IRAG with Iterative Knowledge Retrieval [18.96570718233786]
  GraphRAG has proven highly effective in enhancing the performance of Large Language Models (LLMs) on tasks that require external knowledge.<n>This paper presents Knowledge Graph-Based Iterative Retrieval-Augmented Generation (KG-IRAG), a novel framework that integrates KGs with iterative reasoning.<n>Three new datasets are formed to evaluate KG-IRAG's performance, demonstrating its potential beyond traditional RAG applications.
  arXiv  Detail & Related papers  (2025-03-18T13:11:43Z)
• Retrieval-Augmented Generation with Graphs (GraphRAG) [84.29507404866257]
  Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream task execution by retrieving additional information.<n>Graph, by its intrinsic "nodes connected by edges" nature, encodes massive heterogeneous and relational information.<n>Unlike conventional RAG, the uniqueness of graph-structured data, such as diverse-formatted and domain-specific relational knowledge, poses unique and significant challenges when designing GraphRAG for different domains.
  arXiv  Detail & Related papers  (2024-12-31T06:59:35Z)
• MemoRAG: Boosting Long Context Processing with Global Memory-Enhanced Retrieval Augmentation [60.04380907045708]
  Retrieval-Augmented Generation (RAG) is considered a promising strategy to address this problem.<n>We propose MemoRAG, a novel RAG framework empowered by global memory-augmented retrieval.<n>MemoRAG achieves superior performances across a variety of long-context evaluation tasks.
  arXiv  Detail & Related papers  (2024-09-09T13:20:31Z)
• Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning [87.10396098919013]
  Large Language Models (LLMs) have demonstrated extensive knowledge and remarkable proficiency in temporal reasoning.<n>We propose a Large Language Models-guided Dynamic Adaptation (LLM-DA) method for reasoning on Temporal Knowledge Graphs.<n>LLM-DA harnesses the capabilities of LLMs to analyze historical data and extract temporal logical rules.
  arXiv  Detail & Related papers  (2024-05-23T04:54:37Z)
• Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models [24.417129499480975]
  This paper first proposes a novel generative temporal knowledge graph question answering framework, GenTKGQA. First, we exploit LLM's intrinsic knowledge to mine temporal constraints and structural links in the questions without extra training. Next, we design virtual knowledge indicators to fuse the graph neural network signals of the subgraph and the text representations of the LLM in a non-shallow way.
  arXiv  Detail & Related papers  (2024-02-26T13:47:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.