Enhancing Industry 4.0 standards interoperability via knowledge graphs with natural language processing

Abstract

Industry 4.0 (I4.0) has brought several challenges related to the need to acquire and integrate large amounts of data from multiple sources in order to integrate these elements into an automated manufacturing system. Establishing interoperability is crucial to meet these challenges, and standards development and adoptions play a key role in achieving this. Therefore, academics and industrial stakeholders must join their forces in order to develop methods to enhance interoperability and to mitigate possible conflicts between standards. The aim of this paper is to propose an approach that enhances interoperability between standards through the combined use of Natural Language Processing (NLP) and Knowledge Graphs (KG). In particular, the proposed method is based on the measurement of semantic similarity among the textual content of standards documents belonging to different standardization frameworks. The present study contributes to the research and practice in three ways. First, it fills research gaps concerning the synergy of NLP, KGs and I4.0. Second, it provides an automatic method that improves the process of creating, curating and enriching a KG. Third, it provides qualitative and quantitative evidence of Semantic Interoperability Conflicts (SICs). The experimental results of the application of the proposed method to the I4.0 Standards Knowledge Graph (I40KG) show that different standards are still struggling to use a shared language and that there exists a strong different in the view of I4.0 proposed by the two main standardization frameworks (RAMI and IIRA). By automatically enriching the I40KG with a solid experimental approach, we are paving the way for actionable knowledge which has been extracted from the PDFs and made available in the I40KG.