Current industrial automation systems are facing increasing dynamics. Thus, acquiring and managing heterogeneous data within the Digital Twin to enable decision making is necessary although challenging. Knowledge Graphs unify and relate data, enabling the derivation of new insights. In this contribution, an approach for context-enriched modeling of cyber-physical production systems is proposed, in order to realize a Knowledge Graph enhanced intelligent Digital Twin further considering the context. Therefore, the modeling approach of the Knowledge Graph considers context and serves as a base for the graph embeddings to gain further knowledge about the production system. This knowledge is used within the architecture of the intelligent Digital Twin. The resulting benefits, i.e. diverse manifestations of an improved decision making, are highlighted by the use case of self-organized reconfiguration management.
%0 Generic
%1 muller2022contextenriched
%A Müller, Timo
%A Sahlab, Nada
%A Kamm, Simon
%A Braun, Dominik
%A Köhler, Christian
%A Jazdi, Nasser
%A Weyrich, Michael
%B 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), September, 2022
%D 2022
%K 2022ias ias
%T Context-enriched modeling using Knowledge Graphs for intelligent Digital Twins of Production Systems
%X Current industrial automation systems are facing increasing dynamics. Thus, acquiring and managing heterogeneous data within the Digital Twin to enable decision making is necessary although challenging. Knowledge Graphs unify and relate data, enabling the derivation of new insights. In this contribution, an approach for context-enriched modeling of cyber-physical production systems is proposed, in order to realize a Knowledge Graph enhanced intelligent Digital Twin further considering the context. Therefore, the modeling approach of the Knowledge Graph considers context and serves as a base for the graph embeddings to gain further knowledge about the production system. This knowledge is used within the architecture of the intelligent Digital Twin. The resulting benefits, i.e. diverse manifestations of an improved decision making, are highlighted by the use case of self-organized reconfiguration management.
@conference{muller2022contextenriched,
abstract = {Current industrial automation systems are facing increasing dynamics. Thus, acquiring and managing heterogeneous data within the Digital Twin to enable decision making is necessary although challenging. Knowledge Graphs unify and relate data, enabling the derivation of new insights. In this contribution, an approach for context-enriched modeling of cyber-physical production systems is proposed, in order to realize a Knowledge Graph enhanced intelligent Digital Twin further considering the context. Therefore, the modeling approach of the Knowledge Graph considers context and serves as a base for the graph embeddings to gain further knowledge about the production system. This knowledge is used within the architecture of the intelligent Digital Twin. The resulting benefits, i.e. diverse manifestations of an improved decision making, are highlighted by the use case of self-organized reconfiguration management. },
added-at = {2022-10-19T11:22:03.000+0200},
author = {Müller, Timo and Sahlab, Nada and Kamm, Simon and Braun, Dominik and Köhler, Christian and Jazdi, Nasser and Weyrich, Michael},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2ac1824f44a4f1cb86de58b5665783aaf/taylansngerli},
booktitle = {2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), September, 2022},
interhash = {421410c175a333031368e84c16007861},
intrahash = {ac1824f44a4f1cb86de58b5665783aaf},
keywords = {2022ias ias},
timestamp = {2022-10-19T09:22:03.000+0200},
title = {Context-enriched modeling using Knowledge Graphs for intelligent Digital Twins of Production Systems },
year = 2022
}
