构建知识图谱与大模型结合的平台，加速危险化学品信息获取。
Combining knowledge graphs and LLMs for hazardous chemical information management and reuse
- 整合多源数据构建结构化知识图谱，支持自然语言查询。
- 遵循FAIR原则的数据集可显著缩短信息检索时间与人力成本。
- 适合医疗人员在化学中毒急救中快速决策使用。
人类健康正日益受到持久性有毒化学品暴露的威胁。这些物质常以复杂混合物形式存在，其与多种疾病的关系已在科学研究中得到证实。然而，相关信息分散于多个来源，对人和机器均难以访问。本文评估了当前危险化学品信息的发布与获取实践，提出一种新型平台，旨在紧急情况下高效检索关键化学数据。该平台从多源整合信息并组织为结构化知识图谱，用户可通过Neo4J Bloom可视化界面、仪表盘或聊天机器人进行自然语言查询。研究发现，遵循FAIR原则的数据集能显著减少信息获取的时间与人力投入。文章还总结了平台开发中的经验教训，为数据所有者和发布者提供提升数据可重用性与互操作性的建议。本工作旨在提升医疗专业人员对化学品信息的可及性与可用性，从而支持更优健康结果和面对化学中毒风险时的科学决策。
原文摘要 · Abstract (English)
Human health is increasingly threatened by exposure to hazardous substances, particularly persistent and toxic chemicals. The link between these substances, often encountered in complex mixtures, and various diseases are demonstrated in scientific studies. However, this information is scattered across several sources and hardly accessible by humans and machines. This paper evaluates current practices for publishing/accessing information on hazardous chemicals and proposes a novel platform designed to facilitate retrieval of critical chemical data in urgent situations. The platform aggregates information from multiple sources and organizes it into a structured knowledge graph. Users can access this information through a visual interface such as Neo4J Bloom and dashboards, or via natural language queries using a Chatbot. Our findings demonstrate a significant reduction in the time and effort required to access vital chemical information when datasets follow FAIR principles. Furthermore, we discuss the lessons learned from the development and implementation of this platform and provide recommendations for data owners and publishers to enhance data reuse and interoperability. This work aims to improve the accessibility and usability of chemical information by healthcare professionals, thereby supporting better health outcomes and informed decision-making in the face of patients exposed to chemical intoxication risks.
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