Construction of waste-to-resource knowledge graph for industrial symbiosis identification using large language models.

Circular Economy offers a promising approach to achieve sustainability goals by circulating resources and closing resource loops. Industrial Symbiosis adopts similar concept in industrial systems that reduces raw material consumption and waste production through collaborative waste-to-resource exchanges. While waste-to-resource databases provide valuable knowledge for IS opportunity identification, existing databases are mainly constructed manually and are restricted by their sizes and scalability. In this work, we propose an automated framework to construct a Waste-to-Resource Knowledge Graph (W2RKG) from pertinent research papers using Large Language Models, which enhances coverage, scalability, and standardisation of the resulting database. The framework comprises a Retrieving Module, an Extraction Module, and a Fusion Module, that collectively transform unstructured text into a well-organised knowledge graph. The final constructed database contains 3518 waste entities, 4471 resource entities and 33,679 waste-to-resource relationships. Extensive experiments and evaluation results demonstrate the efficacy of the proposed method and the overall high quality of the constructed database. The study, thereby, contributes an automatic framework for waste-to-resource database construction and provides a readily accessible W2RKG to support Industrial Symbiosis practitioners in identification applications.