AutomationML Ontology: Modeling Cyber-Physical Systems for Industry 4.0

Tracking #: 1855-3068

This paper is currently under review
Authors: 
Olga Kovalenko
Irlán Grangel-González
Marta Sabou
Arndt Lüder
Stefan Biffl
Sören Auer
Maria-Esther Vidal

Responsible editor: 
Aldo Gangemi

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Submission type: 
Ontology Description
Abstract: 
We present an AutomationML ontology (AMLO) that covers the CAEX part of the AutomationML standard. The AutomationML data format facilitates the engineering data exchange during industrial systems design. Having a semantic representation of the AutomationML standard allows industrial practitioners to interlink and integrate heterogeneous data more efficiently and to benefit from the Semantic Web tools and technology stack, while at the same time, using a familiar domain-specific conceptualization. Compared to earlier efforts for semantically representing AutomationML, AMLO (a) covers the entire CAEX standard, and not just portions relevant for a use case; (b) has been developed following best practices for ontology engineering; and (c) is made openly available for the community by following latest guidelines on resource sharing and publishing. We describe AMLO and demonstrate its use in real-life scenarios for improving engineering processes in Cyber-Physical System design.
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Under Review