用知识图谱增强大模型，让循环经济决策更准确可靠。
A Graph-Retrieval-Augmented Generation Framework Enhances Decision-Making in the Circular Economy
- 将自然语言查询转为SPARQL，从工业与废弃物知识图谱中检索事实
- 多跳问答任务中ROUGE-L F1达1.0，远超基线（<0.08）
- 适合政策制定与低碳投资决策者使用，支持可追溯的合规分析
大型语言模型在可持续制造中有潜力，但常虚构工业代码和排放系数，影响监管与投资决策。本文提出CircuGraphRAG，一种基于领域知识图谱的检索增强生成框架，该图谱关联117,380个工业与废物实体，包含分类编码与GWP100排放数据，支持结构化多跳推理。自然语言查询被转换为SPARQL并检索验证子图，确保输出准确可追溯。相比独立大模型和朴素RAG，CircuGraphRAG在单跳与多跳问答任务中表现更优，ROUGE-L F1最高达1.0，而基线均低于0.08；同时响应时间减半，令牌消耗减少16%。该框架提供经过验证、符合监管要求的支持，推动可靠、低碳的资源决策。
原文摘要 · Abstract (English)
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.
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