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dc.contributor.authorBordea, Georgeta
dc.contributor.authorBuitelaar, Paul
dc.date.accessioned2016-06-07T10:56:14Z
dc.date.available2016-06-07T10:56:14Z
dc.date.issued2016-05-28
dc.identifier.citationSilvello, Gianmaria; Bordea, Georgeta; Ferro, Nicola; Buitelaar, Paul; Bogers, Toine (2016) 'Semantic representation and enrichment of information retrieval experimental data' International Journal on Digital Libraries, :1-28.en_IE
dc.identifier.issn1432-1300
dc.identifier.urihttp://hdl.handle.net/10379/5862
dc.descriptionJournal articleen_IE
dc.description.abstractExperimental evaluation carried out in international large-scale campaigns is a fundamental pillar of the scientific and technological advancement of information retrieval (IR) systems. Such evaluation activities produce a large quantity of scientific and experimental data, which are the foundation for all the subsequent scientific production and development of new systems. In this work, we discuss how to semantically annotate and interlink this data, with the goal of enhancing their interpretation, sharing, and reuse. We discuss the underlying evaluation workflow and propose a resource description framework model for those workflow parts. We use expertise retrieval as a case study to demonstrate the benefits of our semantic representation approach. We employ this model as a means for exposing experimental data as linked open data (LOD) on the Web and as a basis for enriching and automatically connecting this data with expertise topics and expert profiles. In this context, a topic-centric approach for expert search is proposed, addressing the extraction of expertise topics, their semantic grounding with the LOD cloud, and their connection to IR experimental data. Several methods for expert profiling and expert finding are analysed and evaluated. Our results show that it is possible to construct expert profiles starting from automatically extracted expertise topics and that topic-centric approaches outperform state-of-the-art language modelling approaches for expert finding.en_IE
dc.description.sponsorshipScience Foundation Ireland grant # SFI/12/RC/2289 (INSIGHT)en_IE
dc.formatapen_IE
dc.language.isoenen_IE
dc.publisherSpringer Berlin Heidelbergen_IE
dc.relation.ispartofInternational Journal on Digital Librariesen
dc.subjectExperimental dataen_IE
dc.subjectExpertise profilingen_IE
dc.subjectExpert searchen_IE
dc.subjectInformation retrieval evaluationen_IE
dc.subjectResource description frameworken_IE
dc.subjectSemantic enrichmenten_IE
dc.titleSemantic representation and enrichment of information retrieval experimental dataen_IE
dc.typeArticleen_IE
dc.date.updated2016-06-07T10:17:46Z
dc.identifier.doi10.1007/s00799-016-0172-8
dc.local.publishedsourcehttp://dx.doi.org/10.1007/s00799-016-0172-8en_IE
dc.description.peer-reviewedNot peer reviewed
dc.contributor.funder|~|1267883|~|
dc.description.embargo2017-05-28
dc.internal.rssid11094547
dc.local.contactGeorgeta Bordea, Deri, Ida Business Park, Nui Galway. Email: gbordea@nuigalway.ie
dc.local.copyrightcheckedNo
dc.local.versionACCEPTED
nui.item.downloads714


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