The World Wide Web Consortium (W3C) has published several recommendations for building and storing ontologies, including the most recent OWL 2 Web Ontology Language (OWL). These initiatives have been followed by practical implementations that popularize OWL in various domains. For example, OWL has been used for conceptual modeling in industrial engineering, and its reasoning facilities are used to provide a wealth of services, e.g. model diagnosis, automated code generation, and semantic integration. More specifically, recent studies have shown that OWL is well suited for harmonizing information of engineering tools stored as AutomationML (AML) files. However, OWL and its tools can be cumbersome for direct use by engineers such that an ontology expert is often required in practice. Although much attention has been paid in the literature to overcome this issue by transforming OWL ontologies from to AML models automatically, dealing with OWL complex classes remains an open research question. In this paper, we introduce the AML concept models for representing OWL complex classes in AutomationML, and present algorithms for the bidirectional translation between OWL complex classes and their corresponding AML concept models. We show that this approach provides an efficient and intuitive interface for nonexperts to visualize, modify, and create OWL complex classes.
In the remainder of the paper, we assume that we are given an AML ontology converted from an AML document following the approach proposed in @cite_0 . Our goal is to develop a modeling approach that enhances native AML models with ontological semantics and a translation procedure between such native AML models and OWL complex classes.
@cite_0
2904842953
Technologies for Industry-4.0 are evolving rapidly, and the term semantics is widely used. Various standardization groups claim that they provide mechanisms to express semantics or allow the integration of semantic information. The emerging data format AutomationML (as IEC 62714) proposes a role-based approach to encode semantics in engineering models and has already standardized fundamental engineering concepts as role classes. However, for concrete data processing tasks the currently standardized role classes are not sufficient to unambiguously express the meaning of various vendor or application specific concepts, while user-defined role classes rely on hand wired semantics encoded in dedicated software, e.g. importer exporters. Yet AutomationML system unit classes represent reusable engineering objects as relational models of AutomationML roles, attributes, interfaces, internal elements and links, which can be used to describe complex user-specific concepts. To enable an automatic machine interpretation of these unstandardized relational models, we transform AutomationML data to a formal and declarative semantic representation using the Web Ontology Language (OWL), and propose a rule mining approach to learn the intended meaning of user selected system unit classes, i.e. to identify the common relational structure shared by the selected engineering objects.