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International Conference on Database and Expert Systems Applications

DEXA 2020: Database and Expert Systems Applications pp 179–194Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12392))

Abstract

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 relations 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.

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Notes

  1. 1.

    https://github.com/i40-Tools/I40KG-Embeddings.

  2. 2.

    \(M_p\) corresponds to a projection matrix \(M_p \in \mathbb {R}^{dxk}\) that projects entities from the entity space to the relation space; further \(p \in \mathbb {R}^k\).

  3. 3.

    https://github.com/i40-Tools/I4.0KG-Embeddings

  4. 4.

    http://glaros.dtc.umn.edu/gkhome/metis/metis/download.

  5. 5.

    https://github.com/SDM-TIB/semEP.

  6. 6.

    https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html.

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Acknowledgments

Ariam Rivas is supported by the German Academic Exchange Service (DAAD). This work has been partially funded by the EU H2020 Projects IASIS (GA 727658) and LAMBDA (GA 809965).

Author information

Authors and Affiliations

  1. L3S, Leibniz University of Hannover, Hanover, Germany

    Ariam Rivas & Maria-Esther Vidal

  2. Robert Bosch Corporate Research GmbH, Renningen, Germany

    Irlán Grangel-González

  3. University of Bonn and Fraunhofer IAIS, Bonn, Germany

    Diego Collarana & Jens Lehmann

  4. TIB Leibniz Information Centre for Science and Technology, Hanover, Germany

    Maria-Esther Vidal

Authors
  1. Ariam Rivas
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  2. Irlán Grangel-González
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  3. Diego Collarana
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  4. Jens Lehmann
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  5. Maria-Esther Vidal
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Corresponding author

Correspondence to Ariam Rivas .

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Editors and Affiliations

  1. Clausthal University of Technology, Clausthal-Zellerfeld, Germany

    Sven Hartmann

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Kotsis

  4. IFS, Vienna University of Technology, Vienna, Wien, Austria

    A Min Tjoa

  5. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Rivas, A., Grangel-González, I., Collarana, D., Lehmann, J., Vidal, ME. (2020). Unveiling Relations in the Industry 4.0 Standards Landscape Based on Knowledge Graph Embeddings. In: Hartmann, S., Küng, J., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2020. Lecture Notes in Computer Science(), vol 12392. Springer, Cham. https://doi.org/10.1007/978-3-030-59051-2_12

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  • DOI: https://doi.org/10.1007/978-3-030-59051-2_12

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