产业污染管理知识图谱构建与应用：以江门市为例

Construction and Application of Industrial Pollution Management Knowledge Graph: Taking Jiangmen City as an Example

摘要: 随着工业化和城市化的快速发展，产业污染问题日益严峻，对环境和人类健康构成了严重威胁。产业污染数据具有多源性和异构性，需要通过知识提取和融合技术，才能应用于产业污染治理和决策支持。本文提出利用知识图谱技术整合和建模产业污染领域的关键数据、实体和关系，以实现产业污染知识的多维度展示，包括概念、属性和实例等。以中国广东省江门市产业污染为例，本文通过知识提取、本体构建和知识存储，对公司、污染物、产品等信息进行了综合处理，构建了一个较为全面的产业污染知识图谱。实验结果表明，本文提出的知识图谱构建方法不仅能够有效且直观地揭示污染场地数据之间潜在关联，而且能为决策者提供数据支持和决策参考，同时也为相关研究和应用领域提供了共享数据。

Abstract: With the rapid development of industrialization and urbanization, the problem of industrial pollution has become increasingly serious, posing a significant threat to the environment and human well-being. Industrial pollution data are multi-sourced and heterogeneous, requiring the use of knowledge extraction and fusion techniques for application in industrial pollution management and decision support. This study proposes a knowledge graph approach to integrate and model key data, entities, and relationships within the field of industrial pollution industrial pollution knowledge in terms of concepts, attributes, and instances. Using Jiangmen City in Guangdong Province, China, as a case study, we constructed a comprehensive industrial pollution knowledge graph by integrating company, pollutant, product, and other relevant information through knowledge extraction, ontology construction, and knowledge storage technology. The experimental results show that our knowledge graph construction method effectively reveals potential associations among polluted site data, providing not only intuitive insights but also valuable data support and decision-making references for decision makers. Additionally, the graph contributes to the broader research community by offering accessible data for related studies and applications.