@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 "One of our main objectives is to extend the target KB ontology with new properties on existing classes. We focus on the use case and argue that our approach will have a significant impact if we manage to identify non-existing properties. This would serve as a proof of concept which can ideally scale up to all kinds of input. In order to assess such potential impact in discovering new relations, we need to address the following question: which extractable relations are not already mapped in DBPO or do not even exist in the raw infobox properties datasets?. Table 4 illustrates an empirical lexicographical study gathered from the Italian Wikipedia soccer player subcorpus (circa 52,000 articles). It contains absolute occurrence frequencies of word stems (in descending order) that are likely to trigger domain-relevant frames, thus providing a rough overview of the extraction potential."; 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 . }