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 .
}