Leveraging Graph Databases for Automated OPC UA Information Model Construction
Authors: 
Rongdong Yu, Yaoyi Zhong, Yunliang Xu, Jie Wen, Quanhong Pan, Wanli Sha, Zhan WangAuthors Info & Claims
Rongdong Yu, Yaoyi Zhong, Yunliang Xu, Jie Wen, Quanhong Pan, Wanli Sha, Zhan WangAuthors Info & ClaimsPages 294 - 299
Abstract
To address the OPC UA transformation requirements of conventional factory data collection systems, we propose an innovative method for the automated construction of OPC UA information models utilizing graph databases. This method not only streamlines the process but also minimizes the need for extensive manual labor and specialized expertise. Drawing from the resemblance between OPC UA information models and OWL ontologies, we have devised mapping rules that facilitate the translation of knowledge graph data into OPC UA format. Leveraging the existing Neo4j database within the factory as the primary information source, we’ve developed an OWL ontology construction module for exporting ontology files. In parallel, an information model construction module has been designed to convert the ontology file into an OPC UA XML file, complete with the OPC UA information model. This XML file adheres to the official OPC UA specification, ensured through the use of the UA-ModelCompiler tool. To validate the effectiveness and viability of this construction method, we conducted functional tests and evaluations using a publicly available database. The results of these tests confirm the feasibility of our approach, marking a significant advancement in OPC UA information model construction for factory data systems.
References
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Published In
December 2023
310 pages
ISBN:9798400709265
DOI:10.1145/3638837
Copyright © 2023 ACM.
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Published: 07 March 2024
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ICNCC 2023
ICNCC 2023: 2023 the 12th International Conference on Networks, Communication and Computing
December 15 - 17, 2023
Osaka, Japan
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