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dc.contributor.authorZheng, Zhuoxun
dc.contributor.authorZhou, Baifan
dc.contributor.authorZhou, Dongzhuoran
dc.contributor.authorCheng, Gong
dc.contributor.authorJimenez-Ruiz, Ernesto
dc.contributor.authorSoylu, Ahmet
dc.contributor.authorKharlamov, Evgeny
dc.date.accessioned2022-09-23T08:05:52Z
dc.date.available2022-09-23T08:05:52Z
dc.date.created2022-08-11T15:38:47Z
dc.date.issued2022-07-20
dc.identifier.citationLecture Notes in Computer Science (LNCS). 2022, 13384 123-128.en_US
dc.identifier.issn0302-9743
dc.identifier.urihttps://hdl.handle.net/11250/3020817
dc.description.abstractIndustrial analytics that includes among others equipment diagnosis and anomaly detection heavily relies on integration of heterogeneous production data. Knowledge Graphs (KGs) as the data format and ontologies as the unified data schemata are a prominent solution that offers high quality data integration and a convenient and standardised way to exchange data and to layer analytical applications over it. However, poor design of ontologies of high degree of mismatch between them and industrial data naturally lead to KGs of low quality that impede the adoption and scalability of industrial analytics. Indeed, such KGs substantially increase the training time of writing queries for users, consume high volume of storage for redundant information, and are hard to maintain and update. To address this problem we propose an ontology reshaping approach to transform ontologies into KG schemata that better reflect the underlying data and thus help to construct better KGs. In this poster we present a preliminary discussion of our on-going research, evaluate our approach with a rich set of SPARQL queries on real-world industry data at Bosch and discuss our findings.en_US
dc.description.sponsorshipThe work was partially supported by the H2020 projects Dome 4.0 (Grant Agreement No. 953163), OntoCommons (Grant Agreement No. 958371), and DataCloud (Grant Agreement No. 101016835) and the SIRIUS Centre, Norwegian Research Council project number 237898.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.ispartofseriesLecture Notes in Computer Science (LNCS);Volume 13384
dc.subjectIndustrial analyticsen_US
dc.subjectOntology reshapingen_US
dc.subjectOntologiesen_US
dc.subjectKnowledge graphsen_US
dc.titleQuery-Based Industrial Analytics over Knowledge Graphs with Ontology Reshapingen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1
dc.identifier.doihttps://doi.org/10.1007/978-3-031-11609-4_23
dc.identifier.cristin2042517
dc.source.journalLecture Notes in Computer Science (LNCS)en_US
dc.source.volume13384en_US
dc.source.issue13384en_US
dc.source.pagenumber5en_US
dc.relation.projectNorges forskningsråd: 237898en_US
dc.relation.projectEC/H2020/953163en_US
dc.relation.projectOntoCommons: 958371en_US
dc.relation.projectDataCloud: 101016835en_US


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