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sub:paragraph c4o:hasContent "In this article, we presented a system that puts into practice our fourfold research contribution: first, we perform (1) N-ary relation extraction thanks to the implementation of Frame Semantics, in contrast to traditional binary approaches; second, we (2) jointly enrich both the T-Box and the A-Box parts of our target KB, through the discovery of candidate relations and the extraction of facts respectively. We achieve this with a (3) shallow layer of NLP technology only, namely grammatical analysis, instead of more sophisticated ones, such as syntactic parsing. Finally, we ensure a (4) fully supervised learning paradigm via an affordable crowdsourcing methodology. Our work concurrently bears the advantages and leaves out the weaknesses of RE and OIE: although we assess it in a closed-domain fashion via a use case (Section 3), the corpus analysis module (Section 5) allows to discover an exhaustive set of relations in an open-domain way. In addition, we overcome the supervision cost bottleneck trough crowdsourcing. Therefore, we believe our approach can represent a trade-off between open-domain high noise and closed-domain high cost.";
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this: dc:created "2019-11-10T18:05:11+01:00"^^xsd:dateTime;
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