Analyzing a Knowledge Graph of Industry 4.0 Standards

Realizing smart factories according to the Industry 4.0 vision requires intelligent human-to-machine and machine-to-machine communication. To achieve this goal, components such as actuators, sensors, and cyber-physical systems along with their data, need to be described; moreover, interoperability conflicts arisen from various semantic representations of these components demand also solutions. To empowering communication in smart factories, a variety of standards and standardization frameworks have been proposed. These standards enable the description of the main properties of components, systems, and processes, as well as interactions between them. Standardization frameworks classify, align, and integrate industrial standards according to their purposes and features. Various standardization frameworks have been proposed all over the world by industrial communities, e.g., RAMI4.0 or IICF. While being expressive to categorize existing standards, standardization frameworks may present divergent classifications of the same standard. Mismatches between standard classifications generate semantic interoperability conflicts that negatively impact the effectiveness of communication in smart factories. In this article, we tackle the problem of standard interoperability across different standardization frameworks, and devise a knowledge-driven approach that allows for the description of standards and standardization frameworks into an Industry 4.0 knowledge graph (140KG). The STO ontology represents properties of standards and standardization frameworks, as well as relationships among them. The 140KG integrates more than 200 standards and four standardization frameworks. To populate the 140KG, the landscape of standards has been analyzed from a semantic perspective and the resulting 140KG represents knowledge expressed in more than 200 industrial related documents including technical reports, research articles, and white papers. Additionally, the I40KG has been linked to existing knowledge graphs and an automated reasoning has been implemented to reveal implicit relations between standards as well as mappings across standardization frameworks. We analyze both the number of discovered relations between standards and the accuracy of these relations. Observed results indicate that both reasoning and linking processes enable for increasing the connectivity in the knowledge graph by up to 80%, whilst up to 96% of the relations can be validated. These outcomes suggest that integrating standards and standardization frameworks into the 140KG enables the resolution of semantic interoperability conflicts, empowering the communication in smart factories.