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Conference papers

Linked Data Annotation and Fusion driven by Data Quality Evaluation

Ioanna Giannopoulou 1 Fatiha Saïs 1 Rallou Thomopoulos 2, 3
2 GRAPHIK - Graphs for Inferences on Knowledge
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : In this work, we are interested in exploring the problem of \emph{data fusion}, starting from reconciled datasets whose objects are linked with semantic sameAs relations. We attempt to merge the often conflicting information of these reconciled objects in order to obtain unified representations that only contain the best quality information.
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Submitted on : Wednesday, May 15, 2019 - 4:45:36 PM
Last modification on : Friday, October 22, 2021 - 3:07:24 PM


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  • HAL Id : lirmm-01097737, version 1
  • PRODINRA : 313566


Ioanna Giannopoulou, Fatiha Saïs, Rallou Thomopoulos. Linked Data Annotation and Fusion driven by Data Quality Evaluation. EGC: Extraction et Gestion des Connaissances, Institut Français du Luxembourg. Luxembourg, LUX., Jan 2015, Luxembourg, Luxembourg. 493 p. ⟨lirmm-01097737⟩



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