Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey

2025-04-08

AI Summary

Large language models (LLMs) suffer from factual hallucinations due to outdated knowledge and training data limitations. While retrieval-augmented generation (RAG) mitigates this issue, the multifaceted roles of graph technologies in RAG remain unstructured and lack a unified framework. This paper proposes the first taxonomy of RAG grounded in “graph functionality,” systematically characterizing graphs’ distinct roles in knowledge organization, semantic retrieval, relational reasoning, and dynamic knowledge updating. We integrate techniques—including knowledge graph embedding, subgraph retrieval, graph neural networks, and graph database optimization—to enable efficient ingestion of structured and semi-structured knowledge. Through a comprehensive analysis of 120+ works, we identify critical bottlenecks such as graph sparsity modeling and real-time update latency, and propose six future directions—including scalable graph indexing and causality-aware retrieval—to bridge interdisciplinary gaps across graph learning, databases, and NLP.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Knowledge Representation Languages

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Large language models (LLMs) struggle with the factual error during inference due to the lack of sufficient training data and the most updated knowledge, leading to the hallucination problem. Retrieval-Augmented Generation (RAG) has gained attention as a promising solution to address the limitation of LLMs, by retrieving relevant information from external source to generate more accurate answers to the questions. Given the pervasive presence of structured knowledge in the external source, considerable strides in RAG have been made to employ the techniques related to graphs and achieve more complex reasoning based on the topological information between knowledge entities. However, there is currently neither unified review examining the diverse roles of graphs in RAG, nor a comprehensive resource to help researchers navigate and contribute to this evolving field. This survey offers a novel perspective on the functionality of graphs within RAG and their impact on enhancing performance across a wide range of graph-structured data. It provides a detailed breakdown of the roles that graphs play in RAG, covering database construction, algorithms, pipelines, and tasks. Finally, it identifies current challenges and outline future research directions, aiming to inspire further developments in this field. Our graph-centered analysis highlights the commonalities and differences in existing methods, setting the stage for future researchers in areas such as graph learning, database systems, and natural language processing.

Problem

• Addressing factual errors in LLMs via graph-based RAG
• Surveying graph roles in RAG for structured knowledge
• Identifying challenges and future directions in graph-enhanced RAG

Innovation

• Graph-based techniques enhance RAG accuracy
• Structured knowledge improves complex reasoning
• Survey reviews graph roles in RAG