@prefix dc: .
@prefix this: .
@prefix sub: .
@prefix xsd: .
@prefix prov: .
@prefix pav: .
@prefix np: .
@prefix doco: .
@prefix 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 "OIE output can indeed be considered structured data compared to free text, but it still lacks of a disambiguation facility: extracted facts generally do not employ unique identifiers (i.e., URIs), thus suffering from intrinsic natural language polysemy (e.g., Jaguar may correspond to the animal or a known car brand). To tackle the issue, [12] propose a framework that clusters OIE facts and maps them to elements of a target KB. Similarly to us, they leverage EL techniques for disambiguation and choose DBpedia as the target KB. Nevertheless, the authors focus on A-Box population, while we also cater for the T-Box part. Moreover, OIE systems are used as a black boxes, in contrast to our full implementation of the extraction pipeline. Finally, relations are still binary, instead of our n-ary ones. Taking as input Wikipedia articles, L EGALO [28] exploits page links manually inserted by editors and attempts to induce the relations between them via NLP. Again, the extracted relations are binary and are not mapped to a target KB for enrichment purposes.";
a doco:Paragraph .
}
sub:provenance {
sub:assertion prov:hadPrimarySource ;
prov:wasAttributedTo .
}
sub:pubinfo {
this: dc:created "2019-11-10T18:05:11+01:00"^^xsd:dateTime;
pav:createdBy .
}