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dc.contributor.authorSedrakyan, Gayane
dc.contributor.authorDe Vocht, Laurens
dc.contributor.authorAlonso, Juncal
dc.contributor.authorEscalante, Marisa
dc.contributor.authorOrue-Echevarria, Leire
dc.contributor.authorMannens, Erik
dc.date.accessioned2018-09-04T07:54:49Z
dc.date.available2018-09-04T07:54:49Z
dc.date.issued2018-01
dc.identifier.citationSedrakyan G., De Vocht L., Alonso J., Escalante M., Orue-Echevarria L. and Mannens E. (2018). Data Harvesting, Curation and Fusion Model to Support Public Service Recommendations for e-Governments.In Proceedings of the 6th International Conference on Model-Driven Engineering and Software Development - Volume 1: AMARETTO, ISBN 978-989-758-283-7, pages 691-698. DOI: 10.5220/0006728206910698en
dc.identifier.isbn978-989758283-7en
dc.identifier.urihttp://hdl.handle.net/11556/606
dc.description.abstractThis work reports on early results from CITADEL project that aims at creating an ecosystem of best practices, tools, and recommendations to transform Public Administrations with more efficient, inclusive and citizen-centric services. The goal of the recommendations is to support Governments to find out why citizens stop using public services, and use this information to re-adjust provision to bring these citizens back in. Furthermore, it will help identifying why citizens are not using a given public service (due to affordability, accessibility, lack of knowledge, embarrassment, lack of interest, etc.) and, where appropriate, use this information to make public services more attractive, so they start using the services. While recommender systems can enhance experiences by providing targeted information, the entry barriers in terms of data acquisition are very high, often limiting recommender solutions to closed systems of user/context models. The main focus of this work is to provide an architectural model that allows harvesting data from various sources, curating datasets that originate from a multitude of formats and fusing them into semantically enhanced data that contain key performance indicators for the utility of e-Government services. The output can be further processed by analytics and/or recommender engines to suggest public service improvement needs.en
dc.description.sponsorshipThis work has been supported by EC funds from CITADEL project - Empowering Citizens To Transform European Public Administrations (H2020-SC6-CULT-COOP-2016-2017, EC Grant Agreement 726755).en
dc.language.isoengen
dc.publisherSciTePressen
dc.titleData Harvesting, Curation and Fusion Model to Support Public Service Recommendations for e-Governmentsen
dc.typeconferenceObjecten
dc.identifier.doi10.5220/0006728206910698en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/726755/EU/Empowering Citizens to TrAnsform European PubLic Administrations/CITADELen
dc.rights.accessRightsembargoedAccessen
dc.subject.keywordsArchitectural Modelen
dc.subject.keywordsRecommendation Generationen
dc.subject.keywordsPublic Administrationen
dc.subject.keywordsPublic Servicesen
dc.subject.keywordsDataen
dc.subject.keywordsData Harvesting
dc.subject.keywordsData Curation
dc.subject.keywordsData Fusion
dc.subject.keywordsLinked Data
dc.subject.keywordsE-Government
dc.page.final698en
dc.page.initial691en
dc.conference.title6th International Conference on Model-Driven Engineering and Software Development, MODELSWARD 2018; Funchal, Madeira; Portugal; 22 January 2018 through 24 January 2018en


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