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A collaboration context ontology to enhance human-related collaboration into Industry 4.0

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Abstract

The advent of Industry 4.0 where humans and intelligent machines coexist, allows machines to assist humans on production lines. During such processes, humans work with other humans and/or machines to produce the required products, forming human-related collaborations. Therefore, Industry 4.0 goes beyond a digital ecosystem by being considered as a System of Information Systems which matches heterogeneous systems. This heterogeneity causes poor information interoperability, weakening the effectiveness of collaboration. This poses an issue: how to facilitate human-related collaborations on production lines into Industry 4.0? Addressing it requires better information interoperability and the definition of indicators that can be used to generate recommendations during collaborations. In this article, we focus on indicators of collaboration context and integrate them into a collaboration context ontology to enhance human-related collaborations into Industry 4.0. We then show how to use it in generating context-aware collaborator recommendations.

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Fig. 1

[adapted from Tay et al. (2018), Saucedo-Martínez et al. (2018) and Alcácer and Cruz-Machado (2019)]

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Notes

  1. https://www.w3.org/wiki/Good_Ontologies.

  2. http://xmlns.com/foaf/spec/.

  3. https://www.w3.org/TR/prov-overview/.

  4. https://www.w3.org/Submission/sioc-spec/.

  5. https://www.w3.org/TR/vcard-rdf/.

  6. A summary of the indirect relations between mcc:UserGroup and the five indicator groups (Goal, Collaborator, Activity, and Resource) is available in Fig. 12.

  7. Notably, the two different ratings, rating\((u, i'_m)\) and rating\((u, i'_h)\), are the given data. We do not discuss how to obtain them in this paper.

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Acknowledgements

The first author is funded by the China Scholarship Council (CSC) from the Ministry of Education of P. R. China.

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Authors and Affiliations

  1. Sorbonne universités, Université de technologie de Compiègne, CNRS UMR 7253, HEUDIASYC, 60203, Compiègne Cedex, France

    Siying Li & Marie-Hélène Abel

  2. Paris-Dauphine University, PSL Research University, CNRS UMR 7243, LAMSADE, 75016, Paris, France

    Elsa Negre

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  1. Siying Li
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  2. Marie-Hélène Abel
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Correspondence to Siying Li.

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Li, S., Abel, MH. & Negre, E. A collaboration context ontology to enhance human-related collaboration into Industry 4.0. Cogn Tech Work 24, 75–91 (2022). https://doi.org/10.1007/s10111-021-00677-w

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