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使用知识图谱表示化学传输模型模拟

Representation of chemistry transport models simulations using knowledge graphs.

作者信息

Illueca Fernández Eduardo, Jara Valera Antonio Jesús, Fernández Breis Jesualdo Tomás

机构信息

Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden.

Department of Informatics and Systems, University of Murcia, IMIB-Pascual Parrilla, Murcia, Spain.

出版信息

J Cheminform. 2025 May 31;17(1):91. doi: 10.1186/s13321-025-01025-0.

DOI:10.1186/s13321-025-01025-0
PMID:40450355
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12126885/
Abstract

Persistent air quality pollution poses a serious threat to human health, and is one of the action points that policy makers should monitor according to the Directive 2008/50/EC. While deploying a massive network of hyperlocal sensors could provide extensive monitoring, this approach cannot generate geospatial continuous data and present several challenges in terms of logistics. Thus, developing accurate and trustable expert systems based on chemistry transport models is a key strategy for environmental protection. However, chemistry transport models present an important lack of standardization, and the formats are not interoperable between different systems, which limits the use for different stakeholders. In this context, semantic technologies provide methods and standards for scientific data and make information readable for expert systems. Therefore, this paper proposes a novel methodology for an ontology driven transformation for CHIMERE simulations, a chemistry transport model, allowing to generate knowledge graphs representing air quality information. It enables the transformation of netCDF files into RDF triples for short term air quality forecasting. Concretely, we utilize the Semantic Web Integration Tool (SWIT) framework for mapping individuals using an ontology as a template. Then, a new ontology for CHIMERE has been defined in this work, reusing concepts for other standards in the state of the art. Our approach demonstrates that RDF files can be created from netCDF in a linear computational time, allowing the scalability for expert systems. In addition, the ontology complains with the OQuaRE quality metrics and can be extended in future extensions to be applied to other chemistry transport models. SCIENTIFIC CONTRIBUTIONS: Development of the first ontology for a chemistry transport model. FAIRification of physical models thanks to the generation of knowledge graphs from netCDF files. The ontology proposed is published in PURL ( https://purl.org/chimere-ontology ) and the knowledge graph generated for a 72-h simulation can be accessed in the following repository: https://doi.org/10.5281/zenodo.13981544 .

摘要

持续的空气质量污染对人类健康构成严重威胁，是政策制定者应根据2008/50/EC指令进行监测的行动要点之一。虽然部署大规模的超本地传感器网络可以提供广泛的监测，但这种方法无法生成地理空间连续数据，并且在物流方面存在诸多挑战。因此，基于化学传输模型开发准确且可靠的专家系统是环境保护的关键策略。然而，化学传输模型存在重要的标准化缺失问题，不同系统之间的格式不具有互操作性，这限制了不同利益相关者的使用。在此背景下，语义技术为科学数据提供了方法和标准，并使信息对专家系统可读。因此，本文提出了一种新颖的方法，用于对化学传输模型CHIMERE模拟进行本体驱动的转换，从而生成表示空气质量信息的知识图谱。它能够将netCDF文件转换为RDF三元组，用于短期空气质量预测。具体而言，我们利用语义网集成工具（SWIT）框架，以本体为模板映射个体。然后，在这项工作中定义了一个新的CHIMERE本体，复用了现有技术中其他标准的概念。我们的方法表明，可以在线性计算时间内从netCDF创建RDF文件，从而实现专家系统的可扩展性。此外，该本体符合OQuaRE质量指标，并且可以在未来的扩展中应用于其他化学传输模型。科学贡献：为化学传输模型开发首个本体。通过从netCDF文件生成知识图谱实现物理模型的FAIR化。所提出的本体发布在PURL（https://purl.org/chimere-ontology）上，并且可以在以下存储库中访问为72小时模拟生成的知识图谱：https://doi.org/10.5281/zenodo.13981544 。

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