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Transformation of semantic knowledge into simulation-based decision support

机译：语义知识转化为基于仿真的决策支持

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摘要

Simulation is capable to cope with the uncertain and dynamic nature of industrial value chains. However, in-depth system expertise is inevitable for mapping objects and constraints from the real world to a virtual model. This knowledge-intensity leads to long development times of respective projects, which contradicts the need for timely decision support. Since more and more companies use industrial knowledge graphs and ontologies to foster their knowledge management, this paper proposes a framework on how to efficiently derive a simulation model from such semantic knowledge bases. As part of the approach, a novel Simulation Ontology provides a standardized meta-model for hybrid simulations. Its instantiation enables the user to come up with a fully parameterized formal simulation model. Newly developed Mapping Rules facilitate this process by providing guidance on how to turn knowledge from existing ontologies, which describe the system to be simulated, into instances of the Simulation Ontology. The framework is completed by a parsing procedure for an automated transformation of this conceptual model into an executable one. This novel modeling approach makes model development more efficient by reducing its complexity. It is validated in a use case implementation from semiconductor manufacturing, where cross-domain knowledge was required in order to model and simulate the impacts of the COVID-19 pandemic on a global supply chain network.
机译：模拟能够应对工业价值链的不确定和动态性质。然而，深入的系统专业知识是不可避免的，用于将真实世界映射到虚拟模型。这种知识强度导致各个项目的长期发展时间，相当于及时决策支持的需求。由于越来越多的公司利用工业知识图和本体来培养他们的知识管理，本文提出了一种关于如何从这种语义知识库有效地推导模拟模型的框架。作为该方法的一部分，一种新型仿真本体提供了一种用于混合模拟的标准化元模型。其实例化使用户能够提出完全参数化的正式仿真模型。新开发的映射规则通过提供关于如何从描述要模拟的系统的知识转向仿真本体的实例的指导来促进此过程。该框架由解析过程完成，用于将该概念模型自动转换为可执行文件。这种新颖的建模方法通过降低其复杂性更有效地使模型发展更有效。它在半导体制造的用例中验证，其中需要跨域知识以模拟和模拟Covid-19大流行对全球供应链网络的影响。

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