将语义学融入研究数据管理：材料科学实验工作流的建模与验证
Integrating Semantics into Research Data Management: Modelling and Validating Materials Science Experiment Workflows
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中文总结 AI 辅助
本研究将语义网技术融入材料科学RDM，通过本体建模关系型RDM系统并构建KG，利用SHACL验证实验工作流一致性，集成UI助力研究人员管理实验。
中文摘要 AI 辅助
在科学环境中融入语义网技术正成为日益流行的研究数据管理（RDM）实践。本体提供灵活、可复用且机器可读的词汇表，用于描述特定领域的研究数据；知识图谱（KG）则支持将异构数据源整合为可互操作的集合。此外，KG还具备额外优势，例如支持使用表达性SPARQL查询，或通过SHACL定义复杂的数据验证规则。本研究描述了利用本体对关系型RDM系统进行建模，并基于该本体构建KG的过程。得益于系统中研究数据与实验工作流高度互联的特性，我们不仅展示了如何从这类特定领域的RDM系统中轻松且可靠地构建高效的KG，还阐明了构建KG如何支持更高级的用例。具体而言，我们对KG中记录的实验工作流的理想对应物进行建模，随后利用这些理想对应物以编程方式生成SHACL形状，用于完全验证实验工作流与规则的一致性。通过将此功能集成到用户界面（UI）中，我们允许研究人员规划、复用、共享并追踪日常实验的进展。
英文摘要
The incorporation of Semantic Web technologies within scientific environments is becoming an increasingly popular Research Data Management (RDM) practice. While ontologies offer flexible, reusable and machine-readable vocabularies to describe domain-specific research data, Knowledge Graphs (KGs) facilitate the integration of heterogeneous data sources into an interoperable collection. Furthermore, KGs offer additional advantages, notably the use of expressive SPARQL queries, or the ability to define complex data validation rules with SHACL. This work describes the modelling of a relational RDM system with an ontology, and the subsequent construction of a KG based on it. Enabled by the highly interconnected nature of research data and experiment workflows present in the system, we not only show how we can easily and reliably build an efficient KG from such domain-specific RDM systems, but also how doing so enables more advanced use cases. This is demonstrated by the modelling of ideal counterparts for the experiment workflows logged in the KG, which are then used to programmatically generate SHACL shapes that fully validate the conformance of the latter. By integrating this functionality within a UI, we allow researchers to plan, reuse, share, and track the progress of their daily experiments.
