Discover Relations in the Industry 4.0 Standards Via Unsupervised Learning on Knowledge Graph Embeddings Academic Article uri icon

abstract

  • Industry 4.0 (I4.0) standards and standardization frameworks provide a unified way to describe smart factories. Standards specify the main components, systems, and processes inside a smart factory and the interaction among all of them. Furthermore, standardization frameworks classify standards according to their functions into layers and dimensions. Albeit informative, frameworks can categorize similar standards differently. As a result, interoperability conflicts are generated whenever smart factories are described with miss-classified standards. Approaches like ontologies and knowledge graphs enable the integration of standards and frameworks in a structured way. They also encode the meaning of the standards, known relations among them, as well as their classification according to existing frameworks. This structured modeling of the I4.0 landscape using a graph data model provides the basis for graph-based analytical methods to uncover alignments among standards. This paper contributes to analyzing the relatedness among standards and frameworks; it presents an unsupervised approach for discovering links among standards. The proposed method resorts to knowledge graph embeddings to determine relatedness among standards-based on similarity metrics. The proposed method is agnostic to the technique followed to create the embeddings and to the similarity measure. Building on the similarity values, community detection algorithms can automatically create communities of highly similar standards. Our approach follows the homophily principle, and assumes that related standards are together in a community. Thus, alignments across standards are predicted and interoperability issues across them are solved. We empirically evaluate our approach on a knowledge graph of 249 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.

authors

publication date

  • 2021

number of pages

  • 11

start page

  • 336

end page

  • 347

volume

  • 2

issue

  • 3