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Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/28025
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dc.contributor.authorSchaible, Johannen_UK
dc.contributor.authorSzekely, Pedroen_UK
dc.contributor.authorScherp, Ansgaren_UK
dc.contributor.editorSack, Hen_UK
dc.contributor.editorBlomqvist, Een_UK
dc.contributor.editord'Aquin, Men_UK
dc.contributor.editorGhidini, Cen_UK
dc.contributor.editorPaolo Ponzetto, Sen_UK
dc.contributor.editorLange, Cen_UK
dc.date.accessioned2018-10-24T14:36:15Z-
dc.date.available2018-10-24T14:36:15Z-
dc.date.issued2016-12-31en_UK
dc.identifier.urihttp://hdl.handle.net/1893/28025-
dc.description.abstractWhen modeling Linked Open Data (LOD), reusing appropriate vocabulary terms to represent the data is difficult, because there are many vocabularies to choose from. Vocabulary term recommendations could alleviate this situation. We present a user study evaluating a vocabulary term recommendation service that is based on how other data providers have used RDF classes and properties in the LOD cloud. Our study compares the machine learning technique Learning to Rank (L2R), the classical data mining approach Association Rule mining (AR), and a baseline that does not provide any recommendations. Results show that utilizing AR, participants needed less time and less effort to model the data, which in the end resulted in models of better quality.en_UK
dc.language.isoenen_UK
dc.publisherSpringer Verlagen_UK
dc.relationSchaible J, Szekely P & Scherp A (2016) Comparing vocabulary term recommendations using association rules and learning to rank: A user study. In: Sack H, Blomqvist E, d'Aquin M, Ghidini C, Paolo Ponzetto S & Lange C (eds.) The Semantic Web. Latest Advances and New Domains. ESWC 2016, volume 9678. Lecture Notes in Computer Science, 9678. European Semantic Web Conference (ESWC) 2016, Crete, Greece, 29.05.2016-02.06.2016. Cham, Switzerland: Springer Verlag, pp. 214-230. https://doi.org/10.1007/978-3-319-34129-3_14en_UK
dc.relation.ispartofseriesLecture Notes in Computer Science, 9678en_UK
dc.rightsThe publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.en_UK
dc.rights.urihttp://www.rioxx.net/licenses/under-embargo-all-rights-reserveden_UK
dc.subjectAssociation ruleen_UK
dc.subjectresource description frameworken_UK
dc.subjectuser studyen_UK
dc.subjectmodeling tasken_UK
dc.subjectassociation rule miningen_UK
dc.titleComparing vocabulary term recommendations using association rules and learning to rank: A user studyen_UK
dc.typeConference Paperen_UK
dc.rights.embargodate2999-12-31en_UK
dc.rights.embargoreason[Schaible et al 2016.pdf] The publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.en_UK
dc.identifier.doi10.1007/978-3-319-34129-3_14en_UK
dc.citation.jtitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_UK
dc.citation.issn0302-9743en_UK
dc.citation.volume9678en_UK
dc.citation.spage214en_UK
dc.citation.epage230en_UK
dc.citation.publicationstatusPublisheden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.author.emailansgar.scherp@stir.ac.uken_UK
dc.citation.btitleThe Semantic Web. Latest Advances and New Domains. ESWC 2016en_UK
dc.citation.conferencedates2016-05-29 - 2016-06-02en_UK
dc.citation.conferencelocationCrete, Greeceen_UK
dc.citation.conferencenameEuropean Semantic Web Conference (ESWC) 2016en_UK
dc.citation.date14/05/2016en_UK
dc.citation.isbn9783319341286en_UK
dc.publisher.addressCham, Switzerlanden_UK
dc.contributor.affiliationLeibniz Institute for Social Sciences (GESIS)en_UK
dc.contributor.affiliationUniversity of Southern Californiaen_UK
dc.contributor.affiliationLeibniz Information Centre for Economics - ZBWen_UK
dc.identifier.scopusid2-s2.0-84979017533en_UK
dc.identifier.wtid1007438en_UK
dc.contributor.orcid0000-0002-2653-9245en_UK
dc.date.accepted2016-02-22en_UK
dcterms.dateAccepted2016-02-22en_UK
dc.date.filedepositdate2018-10-18en_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeConference Paper/Proceeding/Abstracten_UK
rioxxterms.versionVoRen_UK
local.rioxx.authorSchaible, Johann|en_UK
local.rioxx.authorSzekely, Pedro|en_UK
local.rioxx.authorScherp, Ansgar|0000-0002-2653-9245en_UK
local.rioxx.projectInternal Project|University of Stirling|https://isni.org/isni/0000000122484331en_UK
local.rioxx.contributorSack, H|en_UK
local.rioxx.contributorBlomqvist, E|en_UK
local.rioxx.contributord'Aquin, M|en_UK
local.rioxx.contributorGhidini, C|en_UK
local.rioxx.contributorPaolo Ponzetto, S|en_UK
local.rioxx.contributorLange, C|en_UK
local.rioxx.freetoreaddate2266-04-15en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/under-embargo-all-rights-reserved||en_UK
local.rioxx.filenameSchaible et al 2016.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source9783319341286en_UK
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