A Linked Data browser with recommendations

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dc.contributor.authorDurao, Frederico
dc.contributor.authorBridge, Derek G.
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2019-02-20T12:39:34Z
dc.date.available2019-02-20T12:39:34Z
dc.date.issued2018-12-17
dc.date.updated2019-02-20T12:31:33Z
dc.description.abstractIt is becoming more common to publish data in a way that accords with the Linked Data principles. In an effort to improve the human exploitation of this data, we propose a Linked Data browser that is enhanced with recommendation functionality. Based on a user profile, also represented as Linked Data, we propose a technique that we call LDRec that chooses in a personalized way which of the resources that lie within a certain neighbourhood in a Linked Data graph to recommend to the user. The recommendation technique, which is novel, is inspired by a collective classifier known as the Iterative Classification Algorithm. We evaluate LDRec using both an off-line experiment and a user trial. In the off-line experiment, we obtain higher hit rates than we obtain using a simpler classifier. In the user trial, comparing against the same simpler classifier, participants report significantly higher levels of overall satisfaction for LDRec.en
dc.description.statusPeer revieweden
dc.description.urihttp://ictai2018.org/en
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationDurao, F. and Bridge, D. (2018) 'A Linked Data browser with recommendations', 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), Volos, Greece, 5-7 November, pp. 189-196. doi:10.1109/ICTAI.2018.00038en
dc.identifier.doi10.1109/ICTAI.2018.00038
dc.identifier.endpage196en
dc.identifier.isbn978-1-5386-7449-9
dc.identifier.isbn978-1-5386-7450-5
dc.identifier.issn2375-0197
dc.identifier.issn1082-3409
dc.identifier.startpage189en
dc.identifier.urihttps://hdl.handle.net/10468/7527
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.ispartof30th International Conference on Tools with Artificial Intelligence (ICTAI 2018)
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.urihttps://ieeexplore.ieee.org/document/8576036
dc.rights© 2018, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectGraph theoryen
dc.subjectIterative methodsen
dc.subjectLinked Dataen
dc.subjectOnline front-endsen
dc.subjectPattern classificationen
dc.subjectRecommender systemsen
dc.subjectSemantic Weben
dc.subjectUser profileen
dc.subjectLDRecen
dc.subjectLinked Data graphen
dc.subjectRecommendation techniqueen
dc.subjectUser trialen
dc.subjectLinked Data browseren
dc.subjectLinked Data principlesen
dc.subjectRecommendation functionalityen
dc.subjectOff-line experimenten
dc.subjectIterative classificationen
dc.subjectBrowsersen
dc.subjectResource description frameworken
dc.subjectMotion picturesen
dc.subjectData modelsen
dc.subjectToolsen
dc.subjectBrowsingen
dc.subjectRecommendingen
dc.subjectCollectiveen
dc.subjectClassificationen
dc.subjectIterativeen
dc.titleA Linked Data browser with recommendationsen
dc.typeConference itemen
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