TY - GEN
T1 - Ontology Mapping-Based Semantic Reasoning with OPC UA for Heterogeneous Industrial Devices
AU - Bi, Jing
AU - Wu, Rina
AU - Yuan, Haitao
AU - Wang, Ziqi
AU - Zhang, Jia
AU - Zhou, Meng Chu
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The advent of smart manufacturing in Industry 4.0 signifies the arrival of the era of connections. As an excellent communication protocol, Object linking and embedding for Process Control Unified Architecture (OPC UA) can address most semantic heterogeneity issues. However, its semantics are not formally defined at the application layer. To address the information silo problem caused by semantic heterogeneity, a method named Querying of Ontology Mapping-based OPC UA (QOMOU) is proposed. It extracts the information models of OPC UA servers into resource description framework triples, utilizes web ontology language for semantic enrichment and inference, and employs a semantic similarity model for event ontology mapping to improve query efficiency. The method's effectiveness is validated through functional queries using the SPARQL protocol in Apache Jena. The query efficiency is 5% higher on average compared to both structured query and extensible markup languages. Moreover, by employing a keyword-matching algorithm, the query accuracy of the existing heterogeneous data integration scheme is improved by 4% on average. This enhancement can boost the operational efficiency of Internet of Things systems based on the OPC UA architecture.
AB - The advent of smart manufacturing in Industry 4.0 signifies the arrival of the era of connections. As an excellent communication protocol, Object linking and embedding for Process Control Unified Architecture (OPC UA) can address most semantic heterogeneity issues. However, its semantics are not formally defined at the application layer. To address the information silo problem caused by semantic heterogeneity, a method named Querying of Ontology Mapping-based OPC UA (QOMOU) is proposed. It extracts the information models of OPC UA servers into resource description framework triples, utilizes web ontology language for semantic enrichment and inference, and employs a semantic similarity model for event ontology mapping to improve query efficiency. The method's effectiveness is validated through functional queries using the SPARQL protocol in Apache Jena. The query efficiency is 5% higher on average compared to both structured query and extensible markup languages. Moreover, by employing a keyword-matching algorithm, the query accuracy of the existing heterogeneous data integration scheme is improved by 4% on average. This enhancement can boost the operational efficiency of Internet of Things systems based on the OPC UA architecture.
KW - ontology
KW - OPC UA
KW - semantic heterogeneity
KW - semantic similarity
KW - syntactic interoperability
UR - http://www.scopus.com/inward/record.url?scp=85217880294&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85217880294&partnerID=8YFLogxK
U2 - 10.1109/SMC54092.2024.10831352
DO - 10.1109/SMC54092.2024.10831352
M3 - Conference contribution
AN - SCOPUS:85217880294
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 4674
EP - 4679
BT - 2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024
Y2 - 6 October 2024 through 10 October 2024
ER -