@prefix dc: <http://purl.org/dc/terms/> .
@prefix this: <http://purl.org/np/RA9ZhHnWGMMWKMZIbfiGXw5h8MQnUx9BBu2-2YeZ1771Q> .
@prefix sub: <http://purl.org/np/RA9ZhHnWGMMWKMZIbfiGXw5h8MQnUx9BBu2-2YeZ1771Q#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@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 "On the other hand, the DBpedia E XTRACTION F RAMEWORK 7 is pretty much mature when dealing with Wikipedia semi-structured content like infoboxes, links and categories. Nevertheless, unstructured content (typically text) plays the most crucial role, due to the potential amount of extra knowledge it can deliver: to the best of our understanding, no efforts have been carried out to integrate an unstructured data extractor into the framework. For instance, given the Germany football team article, 8 we aim at extracting a set of meaningful facts and structure them in machine-readable statements. The sentence In Euro 1992, Germany reached the final, but lost 0–2 to Denmark would produce a list of triples, such as: (Germany, defeat, Denmark) (defeat, score, 0–2) (defeat, winner, Denmark) (defeat, competition, Euro 1992)";
    a doco:Paragraph .
}

sub:provenance {
  sub:assertion prov:hadPrimarySource <http://dx.doi.org/10.3233/SW-170269>;
    prov:wasAttributedTo <https://orcid.org/0000-0002-5456-7964> .
}

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