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Graphs RAG at Scale: Beyond Retrieval-Augmented Generation With Labeled Property Graphs and Resource Description Framework for Complex and Unknown Search Spaces

Manie Tadayon, Mayank Gupta

Abstract

Recent advances in Retrieval-Augmented Generation (RAG) have revolutionized knowledge-intensive tasks, yet traditional RAG methods struggle when the search space is unknown or when documents are semi-structured or structured. We introduce a novel end-to-end Graph RAG framework that leverages both Labeled Property Graph (LPG) and Resource Description Framework (RDF) architectures to overcome these limitations. Our approach enables dynamic document retrieval without the need to pre-specify the number of documents and eliminates inefficient reranking. We propose an innovative method for converting documents into RDF triplets using JSON key-value pairs, facilitating seamless integration of semi-structured data. Additionally, we present a text to Cypher framework for LPG, achieving over 90% accuracy in real-time translation of text queries to Cypher, enabling fast and reliable query generation suitable for online applications. Our empirical evaluation demonstrates that Graph RAG significantly outperforms traditional embedding-based RAG in accuracy, response quality, and reasoning, especially for complex, semi-structured tasks. These findings establish Graph RAG as a transformative solution for next-generation retrieval-augmented systems.

Graphs RAG at Scale: Beyond Retrieval-Augmented Generation With Labeled Property Graphs and Resource Description Framework for Complex and Unknown Search Spaces

Abstract

Recent advances in Retrieval-Augmented Generation (RAG) have revolutionized knowledge-intensive tasks, yet traditional RAG methods struggle when the search space is unknown or when documents are semi-structured or structured. We introduce a novel end-to-end Graph RAG framework that leverages both Labeled Property Graph (LPG) and Resource Description Framework (RDF) architectures to overcome these limitations. Our approach enables dynamic document retrieval without the need to pre-specify the number of documents and eliminates inefficient reranking. We propose an innovative method for converting documents into RDF triplets using JSON key-value pairs, facilitating seamless integration of semi-structured data. Additionally, we present a text to Cypher framework for LPG, achieving over 90% accuracy in real-time translation of text queries to Cypher, enabling fast and reliable query generation suitable for online applications. Our empirical evaluation demonstrates that Graph RAG significantly outperforms traditional embedding-based RAG in accuracy, response quality, and reasoning, especially for complex, semi-structured tasks. These findings establish Graph RAG as a transformative solution for next-generation retrieval-augmented systems.
Paper Structure (39 sections, 4 figures, 5 tables)

This paper contains 39 sections, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Retrieval-Augmented Generation (RAG)
  4. Graph RAG
  5. Resource Description Frameworks (RDF)
  6. Labelled Property Graphs (LPGs)
  7. Graph Query Language (GQL)
  8. Amazon Neptune
  9. NetworkX
  10. FAISS
  11. Data and Experimental Setup
  12. Dataset
  13. RDF Data Representation
  14. JSON to Text Conversion
  15. Embedding Representations
  16. ...and 24 more sections

Figures (4)

  • Figure 1: Overview of the RDF-based pipeline for Graph RAG.
  • Figure 2: Overview of the LPG-based pipeline for Graph RAG.
  • Figure 3: Sample Example of LPG Node Design
  • Figure 4: Full Graph RDF structure: This figure illustrates the complete end-to-end pipeline, including all agentic components responsible for query rewriting, normalization, similar query generation, and other orchestration steps.