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Unveiling Relations in the Industry 4.0 Standards Landscape Based on Knowledge Graph Embeddings

: Rivas, Ariam; Grangel-González, Irlán; Collarana, Diego; Lehmann, Jens; Vidal, Maria-Esther


Hartmann, S.:
Database and Expert Systems Applications. 31st International Conference, DEXA 2020. Proceedings. Pt.II : Bratislava, Slovakia, September 14-17, 2020, Virtual Conference
Cham: Springer Nature, 2020 (Lecture Notes in Computer Science 12392)
ISBN: 978-3-030-59050-5 (Print)
ISBN: 978-3-030-59051-2 (Online)
ISBN: 978-3-030-59052-9
International Conference on Database and Expert Systems Applications (DEXA) <31, 2020, Online>
International Workshop on Biological Knowledge Discovery from Data (BIOKDD) <11, 2020, Online>
International Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems (IWCFS) <4, 2020, Online>
International Workshop on Machine Learning and Knowledge Graphs (MLKgraphs) <2, 2020, Online>
European Commission EC
H2020; 727658; iASiS
Integration and analysis of heterogeneous big data for precision medicine and suggested treatments for different types of patients
European Commission EC
H2020; 809965; LAMBDA
Learning, Applying, Multiplying Big Data Analytics
Conference Paper
Fraunhofer IAIS ()

Industry 4.0 (I4.0) standards and standardization frameworks have been proposed with the goal of empowering interoperability in smart factories. These standards enable the description and interaction of the main components, systems, and processes inside of a smart factory. Due to the growing number of frameworks and standards, there is an increasing need for approaches that automatically analyze the landscape of I4.0 standards. Standardization frameworks classify standards according to their functions into layers and dimensions. However, similar standards can be classified differently across the frameworks, producing, thus, interoperability conflicts among them. Semantic-based approaches that rely on ontologies and knowledge graphs, have been proposed to represent standards, known relation s among them, as well as their classification according to existing frameworks. Albeit informative, the structured modeling of the I4.0 landscape only provides the foundations for detecting interoperability issues. Thus, graph-based analytical methods able to exploit knowledge encoded by these approaches, are required to uncover alignments among standards. We study the relatedness among standards and frameworks based on community analysis to discover knowledge that helps to cope with interoperability conflicts between standards. We use knowledge graph embeddings to automatically create these communities exploiting the meaning of the existing relationships. In particular, we focus on the identification of similar standards, i.e., communities of standards, and analyze their properties to detect unknown relations. We empirically evaluate our approach on a knowledge graph of I4.0 standards using the Trans* family of embedding models for knowledge graph entities. Our results are promising and suggest that relations among standards can be detected accurately.