Two approaches to the dataset interlinking recommendation problem

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dc.identifier.uri http://dx.doi.org/10.15488/1380
dc.identifier.uri http://www.repo.uni-hannover.de/handle/123456789/1405
dc.contributor.author Lopes, Giseli Rabello
dc.contributor.author Leme, Luiz André P. Paes
dc.contributor.author Pereira Nunes, Bernardo
dc.contributor.author Casanova, Marco Antonio
dc.contributor.author Dietze, Stefan
dc.date.accessioned 2017-04-21T11:19:48Z
dc.date.available 2017-04-21T11:19:48Z
dc.date.issued 2014
dc.identifier.citation Lopes, G.R.; Leme, L.A.P.P.; Pereira Nunes, B.; Casanova, M.A.; Dietze, S.: Two approaches to the dataset interlinking recommendation problem. In: Lecture Notes in Computer Science 8786 (2014), S. 324-339. DOI: https://doi.org/10.1007/978-3-319-11749-2_25
dc.description.abstract Whenever a dataset t is published on the Web of Data, an exploratory search over existing datasets must be performed to identify those datasets that are potential candidates to be interlinked with t. This paper introduces and compares two approaches to address the dataset interlinking recommendation problem, respectively based on Bayesian classifiers and on Social Network Analysis techniques. Both approaches define rank score functions that explore the vocabularies, classes and properties that the datasets use, in addition to the known dataset links. After extensive experiments using real-world datasets, the results show that the rank score functions achieve a mean average precision of around 60%. Intuitively, this means that the exploratory search for datasets to be interlinked with t might be limited to just the top-ranked datasets, reducing the cost of the dataset interlinking process. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-11749-2_25. eng
dc.description.sponsorship EC/FP7/LinkedUp
dc.description.sponsorship CNPq/160326/2012-5
dc.description.sponsorship CNPq/303332/2013-1
dc.description.sponsorship CNPq/557128/2009-9
dc.description.sponsorship FAPERJ/E-26/170028/2008
dc.description.sponsorship FAPERJ/E-26/103.070/2011
dc.description.sponsorship FAPERJ/E-26/101.382/2014
dc.description.sponsorship CAPES/1410827
dc.language.iso eng
dc.publisher Heidelberg : Springer Verlag
dc.relation.ispartofseries Lecture Notes in Computer Science 8786 (2014)
dc.rights Es gilt deutsches Urheberrecht. Das Dokument darf zum eigenen Gebrauch kostenfrei genutzt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.
dc.subject Bayesian classifier eng
dc.subject Data interlinking eng
dc.subject Linked Data eng
dc.subject Recommender systems eng
dc.subject Social networks eng
dc.subject Bayesian networks eng
dc.subject Recommender systems eng
dc.subject Social networking (online) eng
dc.subject Data interlinking eng
dc.subject Exploratory search eng
dc.subject Linked datum eng
dc.subject Rank scores eng
dc.subject Real-world datasets eng
dc.subject Web of datum eng
dc.subject Classification (of information) eng
dc.subject.ddc 004 | Informatik ger
dc.title Two approaches to the dataset interlinking recommendation problem
dc.type article
dc.type Text
dc.relation.essn 0302-9743
dc.relation.doi 10.1007/978-3-319-11749-2_25
dc.bibliographicCitation.volume 8786
dc.bibliographicCitation.firstPage 324
dc.bibliographicCitation.lastPage 339
dc.description.version acceptedVersion
tib.accessRights frei zug�nglich


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