GraphRAG In The Field Of General Question Answering: A Survey

Graph-based Retrieval-Augmented Generation (GraphRAG) is an innovative framework that combines Retrieval-Augmented Generation (RAG) with the structured knowledge represen tation of Knowledge Graphs (KGs), enabling more informed and context-aware text gener ation. Despite its growing potential, research in this area remains underdeveloped, with ex isting surveys mainly covering the integration of Large Language Models (LLMs) and KGs in general. Notably, there is a lack of a task oriented, performance-driven analysis specific to GraphRAG methods. This paper addresses these gaps by presenting a survey specifically focused on GraphRAG methodologies, with a particular emphasis on their application to the downstream task of Question Answering (QA). By centering the analysis on this task, the survey aims to provide practical insights for researchers and practitioners working in QA contexts. It presents a detailed overview of methods, datasets and evaluation metrics, relevant to QA. Additionally, it categorizes ex isting studies and highlights emerging trends and challenges that are most pertinent to this domain. Through this focused and in-depth analysis, this paper seeks to provide a compre hensive overview and basis for the application of GraphRAG in QA.