@prefix dcterms: .
@prefix orcid: .
@prefix this: .
@prefix sub: .
@prefix xsd: .
@prefix prov: .
@prefix pav: .
@prefix np: .
@prefix doco: .
@prefix c4o: .
sub:Head {
this: np:hasAssertion sub:assertion;
np:hasProvenance sub:provenance;
np:hasPublicationInfo sub:pubinfo;
a np:Nanopublication .
}
sub:assertion {
sub:paragraph c4o:hasContent "Simple issues like syntax errors or duplicates can be easily identified and repaired in a fully automatic fash- ion. However, data quality issues in LD are more challenging to detect. Current approaches to tackle these problems still require expert human intervention, e.g., for specifying rules [14] or test cases [21], or fail due to the context-specific nature of quality assessment, which does not lend itself well to general workflows and rules that could be executed by a computer pro- gram. In this paper, we explore an alternative data cu- ration strategy, which is based on crowdsourcing.";
a doco:Paragraph .
}
sub:provenance {
sub:assertion prov:hadPrimarySource ;
prov:wasAttributedTo orcid:0000-0003-0530-4305 .
}
sub:pubinfo {
this: dcterms:created "2019-11-10T12:34:11+01:00"^^xsd:dateTime;
pav:createdBy orcid:0000-0002-7114-6459 .
}