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dc.contributor.authorMuñoz, Emir
dc.contributor.editorSack, Harald and Blomqvist, Eva and d'Aquin, Mathieu and Ghidini, Chiara and Ponzetto, Paolo Simone and Lange, Christoph
dc.date.accessioned2016-09-14T11:38:57Z
dc.date.available2016-09-14T11:38:57Z
dc.date.issued2016-05-14
dc.identifier.citationMuñoz, Emir. (2016). On Learnability of Constraints from RDF Data. In H. Sack, E. Blomqvist, M. d'Aquin, C. Ghidini, P. S. Ponzetto & C. Lange (Eds.), The Semantic Web. Latest Advances and New Domains: 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29 -- June 2, 2016, Proceedings (pp. 834-844). Cham: Springer International Publishing.en_IE
dc.identifier.isbn978-3-319-34129-3
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/10379/6014
dc.description.abstractRDF is structured, dynamic, and schemaless data, which enables a big deal of flexibility for Linked Data to be available in an open environment such as the Web. However, for RDF data, flexibility turns out to be the source of many data quality and knowledge representation issues. Tasks such as assessing data quality in RDF require a different set of techniques and tools compared to other data models. Furthermore, since the use of existing schema, ontology and constraint languages is not mandatory, there is always room for misunderstanding the structure of the data. Neglecting this problem can represent a threat to the widespread use and adoption of RDF and Linked Data. Users should be able to learn the characteristics of RDF data in order to determine its fitness for a given use case, for example. For that purpose, in this doctoral research, we propose the use of constraints to inform users about characteristics that RDF data naturally exhibits, in cases where ontologies (or any other form of explicitly given constraints or schemata) are not present or not expressive enough. We aim to address the problems of defining and discovering classes of constraints to help users in data analysis and assessment of RDF and Linked Data quality.en_IE
dc.description.sponsorshipTOMOE project funded by Fujitsu Laboratories Limited and Insight Centre for Data Analytics at NUI Galwayen_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherSpringer International Publishingen_IE
dc.relation.ispartofESWCen
dc.subjectRDF constraintsen_IE
dc.subjectLinked data miningen_IE
dc.subjectData qualityen_IE
dc.subjectData semanticsen_IE
dc.subjectData analytics
dc.titleOn learnability of constraints from RDF dataen_IE
dc.typeConference Paperen_IE
dc.date.updated2016-09-13T12:37:12Z
dc.identifier.doi10.1007/978-3-319-34129-3_52
dc.local.publishedsourcehttp:dx.doi.org/10.1007/978-3-319-34129-3_52en_IE
dc.description.peer-reviewedpeer-reviewed
dc.contributor.funder|~|1267880|~|
dc.internal.rssid11398933
dc.local.contactEmir Munoz, Deri, Ida Business Park, Lower Dangan, Nui Galway. - Email: e.munoz1@nuigalway.ie
dc.local.copyrightcheckedYes
dc.local.versionSUBMITTED
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