Semantic data integration for industry 4.0 standards

Abstract

Industry 4.0 initiatives have fostered the definition of different standards, e.g., AutomationML or OPC UA, allowing for the specification of industrial objects and for machine-to-machine communication in Smart Factories. Albeit facilitating interoperability at different steps of the production life-cycle, the information models generated from these standards are not semantically defined, making the semantic data integration a challenging problem. We tackle the problems of integrating data from documents specified either using the same or different Industry 4.0 standards, and propose a rule-based framework that combines deductive databases and Semantic Web technologies to effectively solve these problems. As a proof-of-concept, we have developed a Datalogbased representation for AutomationML documents, and a set of rules for identifying semantic heterogeneity problems among these documents. We have empirically evaluated our proposed framework against several benchmarks and the initial results suggest that exploiting deductive and Semantic Web techniques allows for increasing scalability, efficiency, and coherence of models for Industry 4.0 manufacturing environments.