Discover Relations in the Industry 4.0
Standards Via Unsupervised Learning on Knowledge Graph Embeddings (pp326-347)
Ariam
Rivas, Irlan
Grangel-Gonzalez, Diego Collarana,
Jens
Lehmann,
and Maria-esther
Vidal
doi:
https://doi.org/10.26421/JDI2.3-2
Abstracts:
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.
Key words:
Industry 4.0,
Standard, Knowledge Graph, Embedding, Unsupervised Learning