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Knowledge Graphs for Semantically Integrating Cyber-Physical Systems

Abstract:

Cyber-Physical Systems (CPSs) are engineered systems that result from the integration of both physical and computational components designed from different engineering perspectives (e.g., mechanical, electrical, and software). Standards related to Smart Manufacturing (e.g., AutomationML) are used to describe CPS components, as well as to facilitate their integration. Albeit expressive, smart manufacturing standards allow for the representation of the same features in various ways, thus hampering a fully integrated description of a CPS component. We tackle this integration problem of CPS components and propose an approach that captures the knowledge encoded in smart manufacturing standards to effectively describe CPSs. We devise SemCPS, a framework able to combine Probabilistic Soft Logic and Knowledge Graphs to semantically describe both a CPS and its components. We have empirically evaluated SemCPS on a benchmark of AutomationML documents describing CPS components from various perspectives. Results suggest that SemCPS enables not only the semantic integration of the descriptions of CPS components, but also allows for preserving the individual characterization of these components.