DC FieldValueLanguage
dc.contributor.authorHildebrandt, Constantin-
dc.contributor.authorKöcher, Aljosha-
dc.contributor.authorKustner, Christof-
dc.contributor.authorLopez-Enriquez, Carlos Manuel-
dc.contributor.authorMuller, Andreas W.-
dc.contributor.authorCaesar, Birte-
dc.contributor.authorGundlach, Claas Steffen-
dc.contributor.authorFay, Alexander-
dc.date.accessioned2022-04-08T13:10:08Z-
dc.date.available2022-04-08T13:10:08Z-
dc.date.issued2020-07-01-
dc.identifier.issn15455955-
dc.description.abstractCyber-physical systems (CPSs) in the manufacturing domain can be deployed to support monitoring and analysis of production systems of a factory in order to improve, support, or automate processes, such as maintenance or scheduling. When a network of CPS is subject to frequent changes, the semantic interoperability between the CPSs is of special interest in order to avoid manual, tedious, and error-prone information model alignments at runtime. Ontologies are a suitable technology to enable semantic interoperability, as they allow the building of information models that lank machine-readable meaning to information, thus enabling CPSs to mutually understand the shared information. The contribution of this article is twofold. First, we present an ontology building method that is tailored toward the needs of CPSs in the manufacturing domain. For this purpose, we introduce the requirements regarding this method and discuss related research concerning ontology building. The method itself is designed to begin with ontological requirements and to yield a formal ontology. As the reuse of ontologies and other information resources (IRs) is crucial to the success of ontology building projects, we put special emphasis on how to reuse IRs in the CPS domain. Second, we present a reusable set of ontology design patterns that have been developed with the aforementioned method in an industrial use case and illustrate their application in the considered industrial environment. The contribution of this article extends the method introduced, as a postconference paper, by a detailed industrial application. Note to Practitioners-With growing digitalization in industry, the exchange and use of manufacturing-related data are becoming increasingly important to improve, support, or automate processes. Thus, it is necessary to combine information from different data sources that have been designed by different vendors and may, therefore, be heterogeneous in structure and semantics. A system that plans a maintenance worker's daily schedule, for instance, requires information about the status of machines, production plans, and inventory, which resides in other systems, such as programmable logic controllers (PLCs) or databases. When creating such information systems, accessing, searching, and understanding the different data sources is a time-intensive and error-prone procedure due to the heterogeneities of the data sources. Even worse, this procedure has to be repeated for every newly built system and for every newly introduced data source. To allow for eased access, searching, and understanding of these heterogeneous data sources, ontology can be used to integrate all heterogeneous data sources in one schema. This article contributes a method for building such ontologies in the manufacturing domain. Furthermore, a set of ontology design patterns is presented, which can be reused when building ontologies for a domain.de_DE
dc.description.sponsorshipAutomatisierungstechnikde_DE
dc.language.isoengde_DE
dc.publisherIEEEde_DE
dc.relationEclass - Standard für Stammdaten und Semantik für die Digitalisierungde_DE
dc.relation.ispartofIEEE transactions on automation science and engineering : T-ASEde_DE
dc.subjectCyber physical systemde_DE
dc.subjectManufacturing domain knowledgede_DE
dc.subjectOntology buildingde_DE
dc.subjectOntologyde_DE
dc.subjectOntology-based data accessde_DE
dc.subjectSemantic heterogeneityde_DE
dc.subjectCPSde_DE
dc.subjectOBDAde_DE
dc.titleOntology Building for Cyber-Physical Systems: Application in the Manufacturing Domainde_DE
dc.typeArticlede_DE
dc.identifier.doi10.1109/TASE.2020.2991777-
dc.identifier.scopus2-s2.0-85085751213-
dcterms.bibliographicCitation.volume17de_DE
dcterms.bibliographicCitation.issue3de_DE
dcterms.bibliographicCitation.pagestart1266de_DE
dcterms.bibliographicCitation.pageend1282de_DE
dcterms.bibliographicCitation.originalpublisherplacePiscatawayde_DE
dcterms.bibliographicCitation.articlenumber9097408de_DE
local.submission.typeonly-metadatade_DE
dc.identifier.eissn15583783-
dc.description.peerReviewed✅de_DE
dc.type.articleScientific Articlede_DE
item.grantfulltextnone-
item.languageiso639-1en-
item.fulltextNo Fulltext-
item.fulltext_sNo Fulltext-
item.openairetypeArticle-
crisitem.author.deptAutomatisierungstechnik-
crisitem.author.deptAutomatisierungstechnik-
crisitem.author.deptAutomatisierungstechnik-
crisitem.author.orcid0000-0002-1922-654X-
crisitem.author.parentorgFakultät für Maschinenbau und Bauingenieurwesen-
crisitem.author.parentorgFakultät für Maschinenbau und Bauingenieurwesen-
crisitem.author.parentorgFakultät für Maschinenbau und Bauingenieurwesen-
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