  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.
