@prefix this: <http://purl.org/np/RAwTVjKiMv9m_e4cbyGB205hECFeZwBFbAtvhhfQ-JO88> .
@prefix sub: <http://purl.org/np/RAwTVjKiMv9m_e4cbyGB205hECFeZwBFbAtvhhfQ-JO88#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix dc: <http://purl.org/dc/terms/> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix pav: <http://purl.org/pav/> .
@prefix np: <http://www.nanopub.org/nschema#> .
@prefix doco: <http://purl.org/spar/doco/> .
@prefix c4o: <http://purl.org/spar/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 <http://dx.doi.org/10.3233/SW-160239>;
    prov:wasAttributedTo <https://orcid.org/0000-0003-0530-4305> .
}

sub:pubinfo {
  this: dc:created "2019-11-10T12:34:11+01:00"^^xsd:dateTime;
    pav:createdBy <https://orcid.org/0000-0002-7114-6459> .
}