Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Semantic web and machine learning techniques addressing semantic interoperability in Industry 4.0

Semantic web and machine learning techniques addressing semantic interoperability in Industry 4.0 This paper aims to offer a comprehensive examination of the various solutions currently accessible for addressing the challenge of semantic interoperability in cyber physical systems (CPS). CPS is a new generation of systems composed of physical assets with computation capabilities, connected with software systems in a network, exchanging data collected from the physical asset, models (physics-based, data-driven, . . .) and services (reconfiguration, monitoring, . . .). The physical asset and its software system are connected, and they exchange data to be interpreted in a certain context. The heterogeneous nature of the collected data together with different types of models rise interoperability problems. Modeling the digital space of the CPS and integrating information models that support cyber physical interoperability together are required.Design/methodology/approachThis paper aims to identify the most relevant points in the development of semantic models and machine learning solutions to the interoperability problem, and how these solutions are implemented in CPS. The research analyzes recent papers related to the topic of semantic interoperability in Industry 4.0 (I4.0) systems.FindingsSemantic models are key enabler technologies that provide a common understanding of data, and they can be used to solve interoperability problems in Industry by using a common vocabulary when defining these models.Originality/valueThis paper provides an overview of the different available solutions to the semantic interoperability problem in CPS. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Web Information Systems Emerald Publishing

Semantic web and machine learning techniques addressing semantic interoperability in Industry 4.0

Loading next page...
 
/lp/emerald-publishing/semantic-web-and-machine-learning-techniques-addressing-semantic-j9z9aj1F33

References (47)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1744-0084
eISSN
1744-0084
DOI
10.1108/ijwis-03-2023-0046
Publisher site
See Article on Publisher Site

Abstract

This paper aims to offer a comprehensive examination of the various solutions currently accessible for addressing the challenge of semantic interoperability in cyber physical systems (CPS). CPS is a new generation of systems composed of physical assets with computation capabilities, connected with software systems in a network, exchanging data collected from the physical asset, models (physics-based, data-driven, . . .) and services (reconfiguration, monitoring, . . .). The physical asset and its software system are connected, and they exchange data to be interpreted in a certain context. The heterogeneous nature of the collected data together with different types of models rise interoperability problems. Modeling the digital space of the CPS and integrating information models that support cyber physical interoperability together are required.Design/methodology/approachThis paper aims to identify the most relevant points in the development of semantic models and machine learning solutions to the interoperability problem, and how these solutions are implemented in CPS. The research analyzes recent papers related to the topic of semantic interoperability in Industry 4.0 (I4.0) systems.FindingsSemantic models are key enabler technologies that provide a common understanding of data, and they can be used to solve interoperability problems in Industry by using a common vocabulary when defining these models.Originality/valueThis paper provides an overview of the different available solutions to the semantic interoperability problem in CPS.

Journal

International Journal of Web Information SystemsEmerald Publishing

Published: Nov 28, 2023

Keywords: Semantic interoperability; Cyber physical systems; Digital twin; Semantic models; Ontology-based modeling; Machine learning

There are no references for this article.