Relink: Constructing Query-Driven Evidence Graph On-the-Fly for GraphRAG

• URL: http://arxiv.org/abs/2601.07192v1
• Date: Mon, 12 Jan 2026 04:35:23 GMT
• Title: Relink: Constructing Query-Driven Evidence Graph On-the-Fly for GraphRAG
• Authors: Manzong Huang, Chenyang Bu, Yi He, Xingrui Zhuo, Xindong Wu,
• Abstract summary: We argue for a textitreason-and-construct paradigm and propose Relink, a framework that dynamically builds a query-specific evidence graph.<n>Relink achieves significant average improvements of 5.4% in EM and 5.2% in F1 over leading GraphRAG baselines.
• Score: 10.60635000782353
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Graph-based Retrieval-Augmented Generation (GraphRAG) mitigates hallucinations in Large Language Models (LLMs) by grounding them in structured knowledge. However, current GraphRAG methods are constrained by a prevailing \textit{build-then-reason} paradigm, which relies on a static, pre-constructed Knowledge Graph (KG). This paradigm faces two critical challenges. First, the KG's inherent incompleteness often breaks reasoning paths. Second, the graph's low signal-to-noise ratio introduces distractor facts, presenting query-relevant but misleading knowledge that disrupts the reasoning process. To address these challenges, we argue for a \textit{reason-and-construct} paradigm and propose Relink, a framework that dynamically builds a query-specific evidence graph. To tackle incompleteness, \textbf{Relink} instantiates required facts from a latent relation pool derived from the original text corpus, repairing broken paths on the fly. To handle misleading or distractor facts, Relink employs a unified, query-aware evaluation strategy that jointly considers candidates from both the KG and latent relations, selecting those most useful for answering the query rather than relying on their pre-existence. This empowers Relink to actively discard distractor facts and construct the most faithful and precise evidence path for each query. Extensive experiments on five Open-Domain Question Answering benchmarks show that Relink achieves significant average improvements of 5.4\% in EM and 5.2\% in F1 over leading GraphRAG baselines, demonstrating the superiority of our proposed framework.

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