使用大型语言模型构建废弃物转化资源知识图,用于工业共生识别
Lan Zhao1,2, Yajuan Sun3, Junhao Ren1
1School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore.
Nature communications
|December 1, 2025
概括
本研究介绍了使用大型语言模型构建废物到资源知识图 (W2RKG) 的自动化框架. 这通过克服手动数据库的局限性,提高了识别工业共生机会.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 工业生态学 工业生态学
背景情况:
- 循环经济和工业共生通过资源循环和废物减少促进可持续发展.
- 现有的废物到资源数据库通常是手动的,限制了可扩展性和覆盖范围.
- 需要自动化知识提取来增强工业共生 (IS) 应用.
研究的目的:
- 开发一个自动化框架来构建一个废物到资源知识图 (W2RKG).
- 改进废物资源数据库的覆盖范围,可扩展性和标准化.
- 用全面的资源支持工业共生实践者.
主要方法:
- 提出了一个整合检索,提取和融合模块的框架.
- 大型语言模型被用来处理研究论文和提取数据.
- 该框架将非结构化的文本转化为结构化的"废物转化为资源"知识图.
主要成果:
- 构建的W2RKG包含3518个废物实体,4471个资源实体和33679个关系.
- 实验评估证实了该方法的有效性和数据库的高质量.
- 自动化方法显著提高了数据库覆盖范围和可扩展性.
结论:
- 成功开发了W2RKG建设的自动化框架.
- 由此产生的W2RKG为工业共生提供了一个有价值的,可扩展的资源.
- 这有助于通过提高数据可访问性来推进循环经济原则.
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