Multi-relational and Concept Analysis based Knowledge extraction in the Industry 4.0: A systematic mapping

Bibliography Reference

Format:

Nicolás, L., Mario, L., Diego, T., & Hervé, P. (2023). Multi-relational and Concept Analysis based Knowledge extraction in the Industry 4.0: A systematic mapping. IFAC-PapersOnLine, 56(2), 7318–7329. https://doi.org/https://doi.org/10.1016/j.ifacol.2023.10.345

Publication Abstract

Smart Enterprises, Smart Manufacturing, and Cyber-Physical Systems are gaining traction in many industry areas. On top of that, the amounts of available data grow rapidly, and organizations are eager to exploit their advantages. To accomplish that, it is mandatory to have a wide variety of methods and algorithms for knowledge extraction in order to fit the different needs and problems of the industry. In this study, we review and dissect the current state of the art in knowledge extraction applied to smart enterprises, smart manufacturing, and cyber-physical systems. More specifically, we provide a classification of the characteristics of the available methods in the literature according to their applications, and point out areas of improvement.

BibTeX Source Entry

@article{NICOLAS20237318,
  doi = {https://doi.org/10.1016/j.ifacol.2023.10.345},
  url = {https://www.sciencedirect.com/science/article/pii/S2405896323007127},
  issn = {2405-8963},
  note = {22nd IFAC World Congress},
  year = {2023},
  month = {},
  pages = {7318-7329},
  title = {Multi-relational and Concept Analysis based Knowledge extraction in the Industry 4.0: A systematic mapping},
  author = {Leutwyler Nicolás and Lezoche Mario and Torres Diego and Panetto Hervé},
  number = {2},
  volume = {56},
  journal = {IFAC-PapersOnLine},
  abstract = {Smart Enterprises, Smart Manufacturing, and Cyber-Physical Systems are gaining traction in many industry areas. On top of that, the amounts of available data grow rapidly, and organizations are eager to exploit their advantages. To accomplish that, it is mandatory to have a wide variety of methods and algorithms for knowledge extraction in order to fit the different needs and problems of the industry. In this study, we review and dissect the current state of the art in knowledge extraction applied to smart enterprises, smart manufacturing, and cyber-physical systems. More specifically, we provide a classification of the characteristics of the available methods in the literature according to their applications, and point out areas of improvement.},
  keywords = {Enterprise interoperability, AI-based enterprise systems, Systems interoperability, Cyber physical system, Smart factory},
}