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dc.contributor.authorGarofalo, Martina
dc.contributor.authorPellegrino, Maria Angela
dc.contributor.authorAltabba, Abdulrahman
dc.contributor.authorCochez, Michael
dc.contributor.editorDimitrov, Konstantin
dc.date.accessioned2018-11-23T08:19:21Z
dc.date.available2018-11-23T08:19:21Z
dc.date.issued2018
dc.identifier.citationGarofalo, M., Pellegrino, M. A., Altabba, A., & Cochez, M. (2018). Leveraging Knowledge Graph Embedding Techniques for Industry 4.0 Use Cases. In K. Dimitrov (Ed.), <i>Cyber Defence in Industry 4.0 Systems and Related Logistics and IT Infrastructures</i> (pp. 10-26). IOS Press. NATO Science for Peace and Security Series D: Information and Communication Security, 51. <a href="https://doi.org/10.3233/978-1-61499-888-4-10" target="_blank">https://doi.org/10.3233/978-1-61499-888-4-10</a>
dc.identifier.otherCONVID_28715393
dc.identifier.otherTUTKAID_79477
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/60306
dc.description.abstractIndustry is evolving towards Industry 4.0, which holds the promise of increased exibility in manufacturing, better quality and improved productivity. A core actor of this growth is using sensors, which must capture data that can used in unforeseen ways to achieve a performance not achievable without them. However, the complexity of this improved setting is much greater than what is currently used in practice. Hence, it is imperative that the management cannot only be performed by human labor force, but part of that will be done by automated algorithms instead. A natural way to represent the data generated by this large amount of sensors, which are not acting measuring independent variables, and the interaction of the di erent devices is by using a graph data model. Then, machine learning could be used to aid the Industry 4.0 system to, for example, perform predictive maintenance. However, machine learning directly on graphs, needs feature engineering and has scalability issues. In this paper we discuss methods to convert (embed) the graph in a vector space, such that it becomes feasible to use traditional machine learning methods for Industry 4.0 settings.fi
dc.format.extent164
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherIOS Press
dc.relation.ispartofCyber Defence in Industry 4.0 Systems and Related Logistics and IT Infrastructures
dc.relation.ispartofseriesNATO Science for Peace and Security Series D: Information and Communication Security
dc.rightsIn Copyright
dc.subject.otherindustry 4.0
dc.subject.otherknowledge graph
dc.subject.othergraph embedding
dc.titleLeveraging Knowledge Graph Embedding Techniques for Industry 4.0 Use Cases
dc.typebookPart
dc.identifier.urnURN:NBN:fi:jyu-201811144707
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/BookItem
dc.date.updated2018-11-14T13:15:14Z
dc.relation.isbn978-1-61499-887-7
dc.description.reviewstatuspeerReviewed
dc.format.pagerange10-26
dc.relation.issn1874-6268
dc.relation.numberinseries51
dc.type.versionacceptedVersion
dc.rights.copyright© IOS Press, 2018.
dc.rights.accesslevelopenAccessfi
dc.format.contentfulltext
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.3233/978-1-61499-888-4-10


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