SAKey: Scalable almost key discovery in RDF data

Show simple item record Symeonidou, Danai Armant, Vincent Pernelle, Nathalie Sais, Fatiha 2016-04-20T15:50:48Z 2016-04-20T15:50:48Z 2014-10
dc.identifier.citation Symeonidou, D., Armant, V., Pernelle, N. and Sais, F. (2014) "SAKey: Scalable almost key discovery in RDF data", 13th International Semantic Web Conference, ISWC 2014. Riva del Garda, Trento, Italy, 19-23 October, 2014. Springer: The Semantic Web – ISWC 2014, pp. 33-49. DOI: 10.1007/978-3-319-11964-9_3 en
dc.identifier.volume 8796 en
dc.identifier.startpage 33 en
dc.identifier.endpage 49 en
dc.identifier.isbn 978-331911963-2
dc.identifier.issn 03029743
dc.identifier.doi 10.1007/978-3-319-11964-9_3
dc.description.abstract Exploiting identity links among RDF resources allows applications to efficiently integrate data. Keys can be very useful to discover these identity links. A set of properties is considered as a key when its values uniquely identify resources. However, these keys are usually not available. The approaches that attempt to automatically discover keys can easily be overwhelmed by the size of the data and require clean data. We present SAKey, an approach that discovers keys in RDF data in an efficient way. To prune the search space, SAKey exploits characteristics of the data that are dynamically detected during the process. Furthermore, our approach can discover keys in datasets where erroneous data or duplicates exist (i.e., almost keys). The approach has been evaluated on different synthetic and real datasets. The results show both the relevance of almost keys and the efficiency of discovering them. en
dc.description.sponsorship Science Foundation Ireland (Grant No. 12/RC/2289) en
dc.description.uri en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Springer International Publishing en
dc.relation.ispartof 13th International Semantic Web Conference, ISWC 2014. Riva del Garda, Trento, Italy, 19-23 October, 2014
dc.rights © 2014 Springer International Publishing. The final publication is available at Springer via en
dc.rights.uri en
dc.subject Data linking en
dc.subject Identity links en
dc.subject Keys en
dc.subject OWL2 en
dc.subject RDF en
dc.title SAKey: Scalable almost key discovery in RDF data en
dc.type Conference item en
dc.internal.authorcontactother Vincent Armant, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: en
dc.internal.availability Full text available en 2016-01-11T14:14:54Z
dc.description.version Accepted Version en
dc.internal.rssid 332356497
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Lecture Notes in Computer Science en
dc.internal.copyrightchecked No !!CORA!! Authors may self-archive the author’s accepted manuscript of their article in any repository, provided it is only made publicly available 12 months after official publication or later. Furthermore, the author may only post his/her version provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be provided by inserting the DOI number of the article in the following sentence: “The final publication is available at Springer via[insert DOI]”." en
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation Riva del Garda, Trento, Italy en
dc.internal.IRISemailaddress en

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