Research Article Ontology-Based Analysis of Manufacturing Processes: Lessons LearnedfromtheCaseStudyofWireHarnessProduction L´ aszl´ oNagy,Tam´ as Ruppert ,andJ´ anos Abonyi MTA-PE Lend¨ ulet Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, Egyetem u. 10, POB 158, Veszpr´ em H-8200, Hungary CorrespondenceshouldbeaddressedtoTam´ asRuppert;ruppert@abonyilab.com Received 7 June 2021; Revised 13 October 2021; Accepted 25 October 2021; Published 19 November 2021 AcademicEditor:MuhammadJavaid Copyright©2021L´ aszl´ oNagyetal.isisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Effective information management is critical for the development of manufacturing processes. is paper aims to provide an overviewofontologiesthatcanbeutilizedinbuildingIndustry4.0applications.emaincontributionsoftheworkarethatit highlights ontologies that are suitable for manufacturing management and recommends the multilayer-network-based inter- pretation and analysis of ontology-based databases. is article not only serves as a reference for engineers and researchers on ontologies but also presents a reproducible industrial case study that describes the ontology-based model of a wire harness assembly manufacturing process. 1.Introduction Managinginformationanddatafromproductionsystemsis critical for digital transformation, especially in Industry 4.0 applications,wherethehorizontalandverticalintegrationof systemsrequiresmoreefficientdataprocessing.Moreefforts havebeenmadetostandardizethisarea,suchastheANSI/ ISA-95 international standard or RAMI 4.0 (Reference Architectural Model Industrie 4.0) [1]. Furthermore, there are ongoing studies in the field of different methodologies and data structurization that aim to support production- related decision-making processes [2] or create models without simulation software-specific knowledge [3]. For a similar purpose, process mining solutions were also devel- oped to discover, analyze, and improve business processes based on event logs of information systems [4, 5]. Semantic data-based modelling structures the data in a specific logical way [6]. Ontology models also contain se- mantic information to provide a basic meaning of the data and describe their internal relationships [7]. Knowledge graph models provide a framework for data integration, processing, analytics, and sharing as a collection of inter- linked descriptions of entities—objects, events, or concepts [8, 9]. Figure 1 shows the emerging trend of research papers related to ontologies. As can be seen, the technology appearedaround2002,andtherapidlyincreasingnumberof publications in the topic knowledge graphs confirms its success and wide applicability range. Because of the importance of horizontal and vertical integration in Industry 4.0, ontologies are being used in production systems to share information over an increas- ingly wide range [1]. Manufacturing companies are faced with many information sharing tasks such as B2M, M2M, and B2B (communication channels between business (B) and/ormachine(M)units)[10,11].einformationhasto be transferred between information systems, optimization methods,ordigitaltwinsimulators[12].Duetothegrowing demand,thepreviouslyproposeddevelopmentsforISA-95, ISA-88, AutomationML (Automation Markup Language), and B2MML (Business to Manufacturing Markup Lan- guage) industry standards as well as frameworks have in- tensified[1];moreover,thesearebasedonknowledgegraphs or ontologies. Duringthefourthindustrialrevolution,newmethods emerged to deal with this problem, and it can be stated that ontology modelling [13] and knowledge represen- tation are part of the future trends [14], which can be Hindawi Complexity Volume 2021, Article ID 8603515, 21 pages https://doi.org/10.1155/2021/8603515