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Alligator: A Deductive Approach for the Integration of Industry 4.0 Standards

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Knowledge Engineering and Knowledge Management (EKAW 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10024))

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Abstract

Industry 4.0 standards, such as AutomationML, are used to specify properties of mechatronic elements in terms of views, such as electrical and mechanical views of a motor engine. These views have to be integrated in order to obtain a complete model of the artifact. Currently, the integration requires user knowledge to manually identify elements in the views that refer to the same element in the integrated model. Existing approaches are not able to scale up to large models where a potentially large number of conflicts may exist across the different views of an element. To overcome this limitation, we developed Alligator, a deductive rule-based system able to identify conflicts between AutomationML documents. We define a Datalog-based representation of the AutomationML input documents, and a set of rules for identifying conflicts. A deductive engine is used to resolve the conflicts, to merge the input documents and produce an integrated AutomationML document. Our empirical evaluation of the quality of Alligator against a benchmark of AutomationML documents suggest that Alligator accurately identifies various types of conflicts between AutomationML documents, and thus helps increasing the scalability, efficiency, and coherence of models for Industry 4.0 manufacturing environments.

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Notes

  1. 1.

    http://www.eclasscontent.com/index.php?id=27022501&version=9_1&language=en&action=det.

  2. 2.

    http://data.ifs.tuwien.ac.at/aml/ontology#.

  3. 3.

    https://w3id.org/i40/aml/.

  4. 4.

    https://github.com/i40-Tools/AlligatorRules.

  5. 5.

    https://github.com/i40-Tools/AMLGoldStandardGenerator.

  6. 6.

    https://github.com/i40-Tools/HeterogeneityExampleData.

  7. 7.

    https://sewiki.iai.uni-bonn.de/research/pdt/docs/start.

  8. 8.

    https://raw.githubusercontent.com/EIS-Bonn/krextor/master/src/xslt/extract/aml.xsl.

  9. 9.

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

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Acknowledgements

This work has been supported by the German Federal Ministry of Education and Research (BMBF) in the context of the projects LUCID (grant no. 01IS14019C), SDI-X (no. 01IS15035C), and Industrial Data Space (no. 01IS15054).

Author information

Authors and Affiliations

  1. Enterprise Information Systems (EIS), Computer Science, University of Bonn, Bonn, Germany

    Irlán Grangel-González, Diego Collarana, Lavdim Halilaj, Christoph Lange, María-Esther Vidal & Sören Auer

  2. Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Sankt Augustin, Germany

    Irlán Grangel-González, Diego Collarana, Lavdim Halilaj, Steffen Lohmann, Christoph Lange, María-Esther Vidal & Sören Auer

  3. Universidad Simón Bolívar, Caracas, Venezuela

    María-Esther Vidal

Authors
  1. Irlán Grangel-González
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  2. Diego Collarana
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  3. Lavdim Halilaj
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  4. Steffen Lohmann
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  5. Christoph Lange
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  6. María-Esther Vidal
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  7. Sören Auer
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Corresponding author

Correspondence to Irlán Grangel-González .

Editor information

Editors and Affiliations

  1. Linköping University, Linköping, Sweden

    Eva Blomqvist

  2. University of Bologna, Bologna, Italy

    Paolo Ciancarini

  3. University of Bologna, Bologna, Italy

    Francesco Poggi

  4. University of Bologna, Bologna, Italy

    Fabio Vitali

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Grangel-González, I. et al. (2016). Alligator: A Deductive Approach for the Integration of Industry 4.0 Standards. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds) Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science(), vol 10024. Springer, Cham. https://doi.org/10.1007/978-3-319-49004-5_18

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  • DOI: https://doi.org/10.1007/978-3-319-49004-5_18

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