mlsanityData to 14 Oct 2026

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

arXiv 2603.22340 ↗First posted 21 Mar 2026cs.IR cs.AIcited by 0

On the map: Retrieval-augmented generation → Knowledge-graph and structured-knowledge RAG

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 15 of its 34 references that are in the map. First posted on arXiv 21 Mar 2026.

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15 of 34 references.

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Same direction, newest first

  1. 2025In-depth Analysis of Graph-based RAG in a Unified Framework6 Mar
  2. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarization24 Apr

Other references · 13

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  3. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
  4. 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,254
  5. 2023Active Retrieval Augmented Generationcited by 516
  6. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 452
  7. 2024The Faiss librarycited by 376
  8. 2025On the Theoretical Limitations of Embedding-Based Retrievalcited by 104
  9. 2024Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generationcited by 90
  10. 2024A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Lawcited by 74
  11. 2021Graph Learning based Recommender Systems: A Reviewcited by 59
  12. 2016Foundations of Modern Query Languages for Graph Databasescited by 22
1 more
  1. 2021Causal Inference in Educational Systems: A Graphical Modeling Approachcited by 1

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  1. 2025NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes
  2. 2024Graph Retrieval-Augmented Generation: A Survey
  3. 2025MiniRAG: Towards Extremely Simple Retrieval-Augmented Generation
  4. 2026DA-RAG: Dynamic Attributed Community Search for Retrieval-Augmented Generation
  5. 2025RGL: A Graph-Centric, Modular Framework for Efficient Retrieval-Augmented Generation on Graphs
  6. 2026Orion-RAG: Path-Aligned Hybrid Retrieval for Graphless Data
  7. 2025Query-Centric Graph Retrieval Augmented Generation
  8. 2025RAG vs. GraphRAG: A Systematic Evaluation and Key Insights