European Knowledge Acquisition Workshop

EKAW 2016: Knowledge Engineering and Knowledge Management pp 272-287

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

  • Irlán Grangel-González
  • Diego Collarana
  • Lavdim Halilaj
  • Steffen Lohmann
  • Christoph Lange
  • María-Esther Vidal
  • Sören Auer
Conference paper

DOI: 10.1007/978-3-319-49004-5_18

Volume 10024 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
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

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.

Keywords

AutomationML Semantic data integration Industry 4.0 

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Irlán Grangel-González
    • 1
    • 2
  • Diego Collarana
    • 1
    • 2
  • Lavdim Halilaj
    • 1
    • 2
  • Steffen Lohmann
    • 2
  • Christoph Lange
    • 1
    • 2
  • María-Esther Vidal
    • 1
    • 2
    • 3
  • Sören Auer
    • 1
    • 2
  1. 1.Enterprise Information Systems (EIS), Computer ScienceUniversity of BonnBonnGermany
  2. 2.Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS)Sankt AugustinGermany
  3. 3.Universidad Simón BolívarCaracasVenezuela