@prefix this: <http://purl.org/np/RAxpICCSqaYBdcW8guYiql7kyBcDsc3IvmcuFV9D0DbHk> .
@prefix sub: <http://purl.org/np/RAxpICCSqaYBdcW8guYiql7kyBcDsc3IvmcuFV9D0DbHk#> .
@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 "The two settings of the MTurk workers outperformed the baseline approach. The ‘first answer’ setting reports a precision of 0.62, while the ‘majority voting’ achieved a precision of 0.94. The 6% of the links that were not properly classified by the crowd corresponds to those web pages whose con- tent is in a different language than English or, de- spite they are referenced from the Wikipedia article of the subject, their association to the subject is not straightforward. Examples of these cases are the following subjects and links: ‘Frank Stanford’ and http://nwar.com/drakefield/ , ‘Forever Green’ and http://www.stirrupcup.co.uk . We hypothesize that the design of the user interface of the HITs – displaying a preview of the web pages to analyze – helped the workers to easily identify those links containing related content to the triple subject.";
    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 "2019-09-20T18:05:11+01:00"^^xsd:dateTime;
    pav:createdBy <https://orcid.org/0000-0002-7114-6459> .
}