In the following contribution, a method is introduced that integrates domain expert-centric ontology design with the Cross-Industry Standard Process for Data Mining (CRISP-DM). This approach aims to efficiently build an application-specific ontology tailored to the corrective maintenance of Cyber-Physical Systems (CPS). The proposed method is divided into three phases. In phase one, ontology requirements are systematically specified, defining the relevant knowledge scope. Accordingly, CPS life cycle data is contextualized in phase two using domain-specific ontological artifacts. This formalized domain knowledge is then utilized in the CRISP-DM to efficiently extract new insights from the data. Finally, the newly developed data-driven model is employed to populate and expand the ontology. Thus, information extracted from this model is semantically annotated and aligned with the existing ontology in phase three. The applicability of this method has been evaluated in an anomaly detection case study for a modular process plant.

翻译：本文提出一种将领域专家主导的本体设计与跨行业数据挖掘标准流程（CRISP-DM）相融合的方法。该方法旨在高效构建适用于信息物理系统（CPS）纠正性维护的领域专用本体。所提出的方法分为三个阶段：第一阶段系统化地明确本体需求，界定相关知识范畴；第二阶段运用领域特定的本体构件对CPS全生命周期数据进行语境化处理；随后将形式化的领域知识应用于CRISP-DM流程，从而高效地从数据中提取新洞见。最终在第三阶段，利用新构建的数据驱动模型对本体进行实例化与扩展，使模型提取的信息经过语义标注后与现有本体实现对齐。该方法已在模块化过程工厂的异常检测案例研究中完成可行性验证。