dc.contributor.author | Garofalo, Martina | |
dc.contributor.author | Pellegrino, Maria Angela | |
dc.contributor.author | Altabba, Abdulrahman | |
dc.contributor.author | Cochez, Michael | |
dc.contributor.editor | Dimitrov, Konstantin | |
dc.date.accessioned | 2018-11-23T08:19:21Z | |
dc.date.available | 2018-11-23T08:19:21Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Garofalo, 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.other | CONVID_28715393 | |
dc.identifier.other | TUTKAID_79477 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/60306 | |
dc.description.abstract | Industry 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.extent | 164 | |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | IOS Press | |
dc.relation.ispartof | Cyber Defence in Industry 4.0 Systems and Related Logistics and IT Infrastructures | |
dc.relation.ispartofseries | NATO Science for Peace and Security Series D: Information and Communication Security | |
dc.rights | In Copyright | |
dc.subject.other | industry 4.0 | |
dc.subject.other | knowledge graph | |
dc.subject.other | graph embedding | |
dc.title | Leveraging Knowledge Graph Embedding Techniques for Industry 4.0 Use Cases | |
dc.type | bookPart | |
dc.identifier.urn | URN:NBN:fi:jyu-201811144707 | |
dc.contributor.laitos | Informaatioteknologian tiedekunta | fi |
dc.contributor.laitos | Faculty of Information Technology | en |
dc.type.uri | http://purl.org/eprint/type/BookItem | |
dc.date.updated | 2018-11-14T13:15:14Z | |
dc.relation.isbn | 978-1-61499-887-7 | |
dc.description.reviewstatus | peerReviewed | |
dc.format.pagerange | 10-26 | |
dc.relation.issn | 1874-6268 | |
dc.relation.numberinseries | 51 | |
dc.type.version | acceptedVersion | |
dc.rights.copyright | © IOS Press, 2018. | |
dc.rights.accesslevel | openAccess | fi |
dc.format.content | fulltext | |
dc.rights.url | http://rightsstatements.org/page/InC/1.0/?language=en | |
dc.relation.doi | 10.3233/978-1-61499-888-4-10 | |