@prefix dc: <http://purl.org/dc/terms/> .
@prefix this: <http://purl.org/np/RAfKb7CB1FFfCbdBJ8SI8G6tchH4D_HvD7ACDNQdW_EBo> .
@prefix sub: <http://purl.org/np/RAfKb7CB1FFfCbdBJ8SI8G6tchH4D_HvD7ACDNQdW_EBo#> .
@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 "To enable a performance evaluation comparison with the supervised method, we developed a rule-based algorithm that handles the full frame and FEs annotation. The main intuition is to map FEs defined in the frame repository to ontology classes of the target KB: such mapping serves as a set of rule pairs (FE, class), e.g., (WINNER , SoccerClub). In the FrameNet terminology, this is homologous to the assignment of semantic types to FEs: for instance, in the ACTIVITY frame, the AGENT is typed with the generic class Sentient. The idea would allow the implementation of the bottom-up one-step annotation flow described in [18]: to achieve this, we run EL over the input sentences and check whether the attached ontology class metadata appear in the frame repository, thus fulfilling the FE classification task." ;
    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> .
}