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dc.rights.licenseopenen_US
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorAZZI, Rabia
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorDIALLO, Abdourahmane Gayo
IDREF: 112800084
dc.date.accessioned2021-07-02T07:19:45Z
dc.date.available2021-07-02T07:19:45Z
dc.date.issued2020-11-05
dc.date.conference2020-11-05
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/94959
dc.description.abstractEnIn this paper we present AMALGAM, a matching approach to annotate tabular dataset with the use of a knowledge graph, developed in the context of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2020). The ultimate goal is to provide fast and e cient approach to annotate tabular data with entities from a background knowledge. The approach combines lookup and filtering services combined with text pre-processing techniques. Experiments conducted in the context of SemTab 2020 with both Column Type Annotation and Cell Type Annotation tasks showed promising results.
dc.language.isoENen_US
dc.title.enAMALGAM: Making tabular dataset explicit with knowledge graph
dc.typeCommunication dans un congrès avec actesen_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.subject.halInformatique [cs]en_US
bordeaux.page9-16en_US
bordeaux.volume2775en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - U1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERM
bordeaux.conference.titleSemantic Web Challenge on Tabular Data to Knowledge Graph Matching, SemTab 2020en_US
bordeaux.countryfr
bordeaux.title.proceedingCEUR Workshop Proceedingsen_US
bordeaux.teamERIASen_US
bordeaux.conference.cityVirtualen_US
bordeaux.peerReviewedouien_US
hal.identifierhal-03285322
hal.version1
hal.date.transferred2021-07-13T09:34:39Z
hal.exporttrue
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