Graph Retrieval-Augmented Generation: A Survey

• URL: http://arxiv.org/abs/2408.08921v2
• Date: Tue, 10 Sep 2024 15:38:56 GMT
• Title: Graph Retrieval-Augmented Generation: A Survey
• Authors: Boci Peng, Yun Zhu, Yongchao Liu, Xiaohe Bo, Haizhou Shi, Chuntao Hong, Yan Zhang, Siliang Tang,
• Abstract summary: Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. This paper provides the first comprehensive overview of GraphRAG methodologies. We formalize the GraphRAG workflow, encompassing Graph-Based Indexing, Graph-Guided Retrieval, and Graph-Enhanced Generation.
• Score: 28.979898837538958
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. By referencing an external knowledge base, RAG refines LLM outputs, effectively mitigating issues such as ``hallucination'', lack of domain-specific knowledge, and outdated information. However, the complex structure of relationships among different entities in databases presents challenges for RAG systems. In response, GraphRAG leverages structural information across entities to enable more precise and comprehensive retrieval, capturing relational knowledge and facilitating more accurate, context-aware responses. Given the novelty and potential of GraphRAG, a systematic review of current technologies is imperative. This paper provides the first comprehensive overview of GraphRAG methodologies. We formalize the GraphRAG workflow, encompassing Graph-Based Indexing, Graph-Guided Retrieval, and Graph-Enhanced Generation. We then outline the core technologies and training methods at each stage. Additionally, we examine downstream tasks, application domains, evaluation methodologies, and industrial use cases of GraphRAG. Finally, we explore future research directions to inspire further inquiries and advance progress in the field. In order to track recent progress in this field, we set up a repository at \url{https://github.com/pengboci/GraphRAG-Survey}.

Related papers

• G-RAG: Knowledge Expansion in Material Science [0.0]
  Graph RAG integrates graph databases to enhance the retrieval process. We implement an agent-based parsing technique to achieve a more detailed representation of the documents.
  arXiv  Detail & Related papers  (2024-11-21T21:22:58Z)
• LEGO-GraphRAG: Modularizing Graph-based Retrieval-Augmented Generation for Design Space Exploration [18.649082227637066]
  GraphRAG addresses challenges in Retrieval-Augmented Generation (RAG) by leveraging graphs with embedded knowledge to enhance the reasoning capabilities of Large Language Models (LLMs) Despite its promising potential, the GraphRAG community currently lacks a unified framework for fine-grained decomposition of the graph-based knowledge retrieval process. We present LEGO-GraphRAG, a modular framework that decomposes the retrieval process of GraphRAG into three interconnected modules.
  arXiv  Detail & Related papers  (2024-11-06T15:32:28Z)
• Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem [27.904195034688257]
  Existing learning-based methods for solving job shop scheduling problems (JSSP) usually use off-the-shelf GNN models tailored to undirected graphs and neglect the rich and meaningful topological structures of disjunctive graphs (DGs) This paper proposes the topology-aware bidirectional graph attention network (TBGAT) to embed the DG for solving JSSP in a local search framework.
  arXiv  Detail & Related papers  (2024-02-27T15:33:20Z)
• Graph Domain Adaptation: Challenges, Progress and Prospects [61.9048172631524]
  We propose graph domain adaptation as an effective knowledge-transfer paradigm across graphs. GDA introduces a bunch of task-related graphs as source graphs and adapts the knowledge learnt from source graphs to the target graphs. We outline the research status and challenges, propose a taxonomy, introduce the details of representative works, and discuss the prospects.
  arXiv  Detail & Related papers  (2024-02-01T02:44:32Z)
• Graph Prompt Learning: A Comprehensive Survey and Beyond [24.64987655155218]
  This paper presents a pioneering survey on the emerging domain of graph prompts in Artificial General Intelligence (AGI) We propose a unified framework for understanding graph prompt learning, offering clarity on prompt tokens, token structures, and insertion patterns in the graph domain. A comprehensive taxonomy categorizes over 100 works in this field, aligning them with pre-training tasks across node-level, edge-level, and graph-level objectives.
  arXiv  Detail & Related papers  (2023-11-28T05:36:59Z)
• SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning [131.04781590452308]
  We present SimTeG, a frustratingly Simple approach for Textual Graph learning. We first perform supervised parameter-efficient fine-tuning (PEFT) on a pre-trained LM on the downstream task. We then generate node embeddings using the last hidden states of finetuned LM.
  arXiv  Detail & Related papers  (2023-08-03T07:00:04Z)
• A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal [57.8455911689554]
  Knowledge graph reasoning (KGR) aims to deduce new facts from existing facts based on mined logic rules underlying knowledge graphs (KGs) It has been proven to significantly benefit the usage of KGs in many AI applications, such as question answering, recommendation systems, and etc.
  arXiv  Detail & Related papers  (2022-12-12T08:40:04Z)
• Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities [128.55790219377315]
  Graph neural networks have emerged as a leading architecture for many graph-level tasks. graph pooling is indispensable for obtaining a holistic graph-level representation of the whole graph.
  arXiv  Detail & Related papers  (2022-04-15T04:02:06Z)
• Few-Shot Learning on Graphs: A Survey [92.47605211946149]
  Graph representation learning has attracted tremendous attention due to its remarkable performance in many real-world applications. semi-supervised graph representation learning models for specific tasks often suffer from label sparsity issue. Few-shot learning on graphs (FSLG) has been proposed to tackle the performance degradation in face of limited annotated data challenge.
  arXiv  Detail & Related papers  (2022-03-17T13:21:11Z)
• Graph Learning based Recommender Systems: A Review [111.43249652335555]
  Graph Learning based Recommender Systems (GLRS) employ advanced graph learning approaches to model users' preferences and intentions as well as items' characteristics for recommendations. We provide a systematic review of GLRS, by discussing how they extract important knowledge from graph-based representations to improve the accuracy, reliability and explainability of the recommendations.
  arXiv  Detail & Related papers  (2021-05-13T14:50:45Z)

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.