@prefix dc: <http://purl.org/dc/terms/> .
@prefix this: <http://purl.org/np/RA_orre19ZnWYFdG9ilyREwn36k4rwASuFjEGnZFI-FWQ> .
@prefix sub: <http://purl.org/np/RA_orre19ZnWYFdG9ilyREwn36k4rwASuFjEGnZFI-FWQ#> .
@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 "First, we observe a lack of ontology property usage in 4 out of 7 classes, probably due to missing mappings between Wikipedia template attributes and DBPO. On the other hand, the ontology properties have a more homogenous distribution compared to the raw ones: this serves as an expected proof of concept, since the main purpose of DBPO and the ontology mappings is to merge heterogenous and multilingual Wikipedia template attributes into a unique representation. In average, most raw properties are concentrated below coverage and frequency threshold values of 0.8 and 4 respectively: this means that roughly 80% of them have a significantly low usage, further highlighted by the log scale. While ontology properties are better distributed, most still do not reach a high coverage/frequency tradeoff, except for SoccerPlayer, which benefits from both rich data (cf. Section 3) and mappings. 28";
    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 "2019-11-10T18:05:11+01:00"^^xsd:dateTime;
    pav:createdBy <https://orcid.org/0000-0002-7114-6459> .
}