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dc.contributor.authorDietze, Stefan
dc.contributor.authorTaibi, Davide
dc.contributor.authorYu, Ran
dc.contributor.authorBarker, Phil
dc.contributor.authord’Aquin, Mathieu
dc.date.accessioned2017-10-09T12:29:09Z
dc.date.available2017-10-09T12:29:09Z
dc.date.issued2017-04-03
dc.identifier.citationDietze, Stefan, Taibi, Davide, Yu, Ran, Barker, Phil, & d'Aquin, Mathieu. (2017). Analysing and Improving Embedded Markup of Learning Resources on the Web. Paper presented at the Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia.en_IE
dc.identifier.urihttp://hdl.handle.net/10379/6893
dc.description.abstractWeb-scale reuse and interoperability of learning resources have been major concerns for the technology-enhanced learning community. While work in this area traditionally focused on learning resource metadata, provided through learning resource repositories, the recent emergence of structured entity markup on the Web through standards such as RDFa and Microdata and initiatives such as schema.org, has provided new forms of entitycentric knowledge, which is so far under-investigated and hardly exploited. The Learning Resource Metadata Initiative (LRMI) provides a vocabulary for annotating learning resources through schema.org terms. Although recent studies have shown markup adoption by approximately 30% of all Web pages, understanding of the scope, distribution and quality of learning resources markup is limited. We provide the first public corpus of LRMI extracted from a representative Web crawl together with an analysis of LRMI adoption on the Web, with the goal to inform data consumers as well as future vocabulary refinements through a thorough understanding of the use as well as misuse of LRMI vocabulary terms. While errors and schema misuse are frequent, we also discuss a set of simple heuristics which significantly improve the accuracy of markup, a prerequisite for reusing learning resource metadata sourced from markup.en_IE
dc.description.sponsorshipThis work has been partially supported by the H2020 programme of the European Union under grant agreement No 687916 – AFEL project (http://afel-project.eu/) and the COST Action KEYSTONE (IC1302).en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherACMen_IE
dc.relation.ispartofProceedings of the 26th International Conference on World Wide Web Companionen
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/
dc.subjectLRMIen_IE
dc.subjectSchema.orgen_IE
dc.subjectWeb markupen_IE
dc.subjectLearning resourcesen_IE
dc.titleAnalysing and improving embedded markup of learning resources on the weben_IE
dc.typeConference Paperen_IE
dc.date.updated2017-10-05T14:44:06Z
dc.identifier.doi10.1145/3041021.3054160
dc.local.publishedsourcehttps://dl.acm.org/citation.cfm?id=3054160en_IE
dc.description.peer-reviewednon-peer-reviewed
dc.internal.rssid13252791
dc.local.contactMathieu D'Aquin. Email: mathieu.daquin@nuigalway.ie
dc.local.copyrightcheckedYes
dcterms.projectinfo:eu-repo/grantAgreement/EC/H2020::RIA/687916/EU/AFEL - Analytics For Everyday Learning/AFEL
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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland