What the Semantic Web Can Do for Cognitive Digital Twins: Challenges and Opportunities

Bibliography Reference

Format:

Lezoche, M., & Torres, D. (2025). What the Semantic Web Can Do for Cognitive Digital Twins: Challenges and Opportunities. In V. Agredo-Delgado, P. H. Ruiz, & C. A. Meneses Escobar (Eds.), Collaboration in Knowledge Discovery and Decision Making (pp. 224–237). Springer Nature Switzerland.

Publication Abstract

The Cognitive Digital Twin is an advanced version of the Digital Twin model. It integrates cognitive computing technologies to create systems that not only connect but also reason, learn from past experiences, and make informed decisions. The integration of machine learning algorithms and artificial intelligence allows cognitive digital twins to process and interpret data. This cognitive capability enables the digital twin to function with a layer of intelligence that mimics human cognitive abilities, making the system adaptable to its environment and capable of handling complex decision-making processes autonomously. The cognitive features of cognitive digital twins are crucial as they enable the system to predict future states, identify potential problems before they occur, and suggest mitigating actions. Furthermore, the use of semantic web technologies can facilitate advanced analytics and machine learning within cognitive digital twins. This article offers a rapid literature analysis of how Semantic Web approaches can support several aspects of cognitive digital twins models.

BibTeX Source Entry

@inproceedings{lezoche_what_2025,
  doi = {},
  isbn = {978-3-031-91690-8},
  note = {},
  year = {2025},
  month = {},
  pages = {224--237},
  title = {What the Semantic Web Can Do for Cognitive Digital Twins: Challenges and Opportunities},
  author = {Lezoche, Mario and Torres, Diego},
  editor = {Agredo-Delgado, Vanessa and Ruiz, Pablo H. and Meneses Escobar, Carlos Augusto},
  address = {Cham},
  ranking = {},
  abstract = {The Cognitive Digital Twin is an advanced version of the Digital Twin model. It integrates cognitive computing technologies to create systems that not only connect but also reason, learn from past experiences, and make informed decisions. The integration of machine learning algorithms and artificial intelligence allows cognitive digital twins to process and interpret data. This cognitive capability enables the digital twin to function with a layer of intelligence that mimics human cognitive abilities, making the system adaptable to its environment and capable of handling complex decision-making processes autonomously. The cognitive features of cognitive digital twins are crucial as they enable the system to predict future states, identify potential problems before they occur, and suggest mitigating actions. Furthermore, the use of semantic web technologies can facilitate advanced analytics and machine learning within cognitive digital twins. This article offers a rapid literature analysis of how Semantic Web approaches can support several aspects of cognitive digital twins models.},
  booktitle = {Collaboration in Knowledge Discovery and Decision Making},
  publisher = {Springer Nature Switzerland},
  organization = {},
}