In the engineering and manufacturing domain, there is currently an atmosphere of departure to a new era of digitized production. In different regions, initiatives in these directions are known under different names, such as industrie du futur in France, industrial internet in the US or Industrie 4.0 in Germany. While the vision of digitizing production and manufacturing gained much traction lately, it is still relatively unclear how this vision can actually be implemented with concrete standards and technologies. Within the German Industry 4.0 initiative, the concept of an Administrative Shell was devised to respond to these requirements. The Administrative Shell is planned to provide a digital representation of all information being available about and from an object which can be a hardware system or a software platform. In this paper, we present an approach to develop such a digital representation based on semantic knowledge representation formalisms such as RDF, RDF Schema and OWL. We present our concept of a Semantic I4.0 Component which addresses the communication and comprehension challenges in Industry 4.0 scenarios using semantic technologies. Our approach is illustrated with a concrete example showing its benefits in a real-world use case.

In recent years, the amount of data has increased exponentially, and knowledge graphs have gained attention as data structures to integrate data and knowledge harvested from myriad data sources. However, data complexity issues like large volume, high-duplicate rate, and heterogeneity usually characterize these data sources, being required data management tools able to address the negative impact of these issues on the knowledge graph creation process. In this paper, we propose the SDM-RDFizer, an interpreter of the RDF Mapping Language (RML), to transform raw data in various formats into an RDF knowledge graph. SDM-RDFizer implements novel algorithms to execute the logical operators between mappings in RML, allowing thus to scale up to complex scenarios where data is not only broad but has a high-duplication rate. We empirically evaluate the SDM-RDFizer performance against diverse testbeds with diverse configurations of data volume, duplicates, and heterogeneity. The observed results indicate that SDM-RDFizer is two orders of magnitude faster than state of the art, thus, meaning that SDM-RDFizer an interoperable and scalable solution for knowledge graph creation. SDM-RDFizer is publicly available as a resource through a Github repository and a DOI.

Industry 4.0 is a global endeavor of automation and data exchange to create smart factories maximizing production capabilities and allowing for new business models. The Reference Architecture Model for Industry 4.0 (RAMI 4.0) describes the core aspects of Industry 4.0 and defines Administration Shells as digital representations of Industry 4.0 components. In this paper, we present an approach to model and implement Industry 4.0 components with the Resource Description Framework (RDF). The approach addresses the challenges of interoperable communication and machine comprehension in Industry 4.0 settings using semantic technologies. We show how related standards and vocabularies, such as IEC 62264, eCl@ss, and the Ontology of Units of Measure (OM), can be utilized along with the RDF-based representation of the RAMI 4.0 concepts. Finally, we demonstrate the applicability and benefits of the approach using an example from a real-world use case

No abstract