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

2026-03-21

AI Summary

This work proposes an end-to-end graph-based retrieval-augmented generation (RAG) framework that addresses the limitations of traditional RAG methods in efficiently retrieving relevant information within unknown search spaces or when handling semi-structured and structured documents. By integrating labeled property graphs (LPGs) with the Resource Description Framework (RDF), the approach automatically converts JSON key-value pairs into RDF triples to incorporate semi-structured data. It further introduces a text-to-Cypher query generation mechanism, enabling real-time, high-precision graph retrieval without requiring a predefined number of source documents. Eliminating inefficient re-ranking steps, the method significantly enhances answer accuracy, reasoning capability, and overall response quality, demonstrating particularly strong performance in complex semi-structured tasks and online scenarios.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Description Logics

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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.

Problem

• Retrieval-Augmented Generation
• unknown search spaces
• semi-structured data
• structured data
• knowledge-intensive tasks

Innovation

• Graph RAG
• Labeled Property Graph
• Resource Description Framework
• RDF Triplets
• Text-to-Cypher