A Graph-Retrieval-Augmented Generation Framework Enhances Decision-Making in the Circular Economy

TL;DR

This paper presents CircuGraphRAG, a retrieval-augmented generation framework that enhances LLMs with a domain-specific knowledge graph for the circular economy, improving accuracy, traceability, and efficiency in decision-making.

Contribution

The paper introduces CircuGraphRAG, a novel RAG framework that grounds LLM outputs in a large, structured knowledge graph for the circular economy, reducing hallucinations and increasing reliability.

Findings

CircuGraphRAG outperforms baseline models in question answering accuracy.

It halves response time and reduces token usage by 16%.

Provides fact-checked, regulatory-ready support for circular economy planning.

Abstract

Large language models (LLMs) hold promise for sustainable manufacturing, but often hallucinate industrial codes and emission factors, undermining regulatory and investment decisions. We introduce CircuGraphRAG, a retrieval-augmented generation (RAG) framework that grounds LLMs outputs in a domain-specific knowledge graph for the circular economy. This graph connects 117,380 industrial and waste entities with classification codes and GWP100 emission data, enabling structured multi-hop reasoning. Natural language queries are translated into SPARQL and verified subgraphs are retrieved to ensure accuracy and traceability. Compared with Standalone LLMs and Naive RAG, CircuGraphRAG achieves superior performance in single-hop and multi-hop question answering, with ROUGE-L F1 scores up to 1.0, while baseline scores below 0.08. It also improves efficiency, halving the response time and reducing token usage by 16% in representative tasks. CircuGraphRAG provides fact-checked, regulatory-ready support for circular economy planning, advancing reliable, low-carbon resource decision making.