About https://w3id.org/scholarlydata/inproceedings/iswc-2019-resource-252

Subject: https://w3id.org/scholarlydata/inproceedings/iswc-2019-resource-252Property: http://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://purl.org/spar/fabio/ProceedingsPaperhttp://www.w3.org/2002/07/owl#Thinghttp://www.w3.org/2000/01/rdf-schema#Resourcehttp://www.ontologydesignpatterns.org/ont/dul/DUL.owl#InformationObjecthttp://www.ontologydesignpatterns.org/ont/dul/DUL.owl#Objecthttp://www.ontologydesignpatterns.org/ont/dul/DUL.owl#SocialObjecthttps://w3id.org/scholarlydata/ontology/conference-ontology.owl#InProceedingsProperty: http://www.w3.org/2000/01/rdf-schema#labelDBpedia FlexiFusion - Best of Wikipedia > Wikidata > Your DataProperty: http://swrc.ontoware.org/ontology#abstractData quality improvement of DBpedia (or datasets in general) has been in the focus of many publications in the past years with topics covering both knowledge enrichment techniques such as type learning, taxonomy generation, interlinking as well as error detection strategies such as property or value outlier detection, type checking, ontology constraints, unit-tests, to name just a few. The concrete innovation of the DBpedia FlexiFusion approach is to massively cut down engineering workload to apply any of the vast methods available in shorter time and also make it easier to produce customized DBpedias. While FlexiFusion is flexible to accommodate other use cases, our main use case in this paper is the generation of richer language-specific DBpedias for the 20+ DBpedia chapters, which we demonstrate on the Catalan DBpedia. In this paper we define a set of quality metrics and evaluate them for Wikidata and DBpedia datasets of several language chapters. Moreover, we show that a quality-driven knowledge fusion approach of theses datasets increases data size, richness as well as quality.Property: http://purl.org/dc/elements/1.1/creatorhttps://w3id.org/scholarlydata/person/daniel-obraczkahttps://w3id.org/scholarlydata/person/jens-lehmannhttps://w3id.org/scholarlydata/person/marvin-hoferhttps://w3id.org/scholarlydata/person/sebastian-hellmannhttps://w3id.org/scholarlydata/person/johannes-freyProperty: http://purl.org/dc/elements/1.1/subjectdata fusion quality assessment data management provenanceProperty: http://purl.org/dc/elements/1.1/titleDBpedia FlexiFusion - Best of Wikipedia > Wikidata > Your DataProperty: http://purl.org/ontology/bibo/authorListhttps://w3id.org/scholarlydata/authorlist/iswc-2019-resource-252Property: http://xmlns.com/foaf/0.1/makerhttps://w3id.org/scholarlydata/person/sebastian-hellmannhttps://w3id.org/scholarlydata/person/johannes-freyhttps://w3id.org/scholarlydata/person/marvin-hoferhttps://w3id.org/scholarlydata/person/jens-lehmannhttps://w3id.org/scholarlydata/person/daniel-obraczkaProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#abstractData quality improvement of DBpedia (or datasets in general) has been in the focus of many publications in the past years with topics covering both knowledge enrichment techniques such as type learning, taxonomy generation, interlinking as well as error detection strategies such as property or value outlier detection, type checking, ontology constraints, unit-tests, to name just a few. The concrete innovation of the DBpedia FlexiFusion approach is to massively cut down engineering workload to apply any of the vast methods available in shorter time and also make it easier to produce customized DBpedias. While FlexiFusion is flexible to accommodate other use cases, our main use case in this paper is the generation of richer language-specific DBpedias for the 20+ DBpedia chapters, which we demonstrate on the Catalan DBpedia. In this paper we define a set of quality metrics and evaluate them for Wikidata and DBpedia datasets of several language chapters. Moreover, we show that a quality-driven knowledge fusion approach of theses datasets increases data size, richness as well as quality.Property: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#hasAuthorListhttps://w3id.org/scholarlydata/authorlist/iswc-2019-resource-252Property: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#isPartOfhttps://w3id.org/scholarlydata/conference/iswc/2019/proceedingsProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#keyworddata fusion quality assessment data management provenanceProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#titleDBpedia FlexiFusion - Best of Wikipedia > Wikidata > Your DataProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#relatesToEventhttps://w3id.org/scholarlydata/talk/iswc-2019-resource-252
Subject: https://w3id.org/scholarlydata/conference/iswc/2019/proceedingsProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#hasParthttps://w3id.org/scholarlydata/inproceedings/iswc-2019-resource-252
Subject: https://w3id.org/scholarlydata/person/daniel-obraczkaProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc-2019-resource-252
Subject: https://w3id.org/scholarlydata/person/marvin-hoferProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc-2019-resource-252
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Subject: https://w3id.org/scholarlydata/person/sebastian-hellmannProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc-2019-resource-252
Subject: https://w3id.org/scholarlydata/person/johannes-freyProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc-2019-resource-252