@prefix dc: <http://purl.org/dc/terms/> .
@prefix this: <http://purl.org/np/RA9CBH6wja6lUbZIUpdkJivIYLLZ_6w1ET24g51N8UYSY> .
@prefix sub: <http://purl.org/np/RA9CBH6wja6lUbZIUpdkJivIYLLZ_6w1ET24g51N8UYSY#> .
@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 "Although the borders are blurred, nowadays we can distinguish two principal fields in Information Extraction, namely Relation Extraction (RE) and Open In- formation Extraction (OIE). While both aim at structuring information in the form of relations between items, their difference relies in the relations set size, either fixed or potentially infinite. It is commonly argued that the main OIE drawback is the generation of noisy data [11,32], while RE is usually more accurate, but requires expensive supervision in terms of language resources [2,30,32]." ;
    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> .
}