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Towards Enriching DBpedia from Vertical Enumerative Structures Using a Distant Learning Approach

Mouna Kamel 1 Cassia Trojahn 1
1 IRIT-MELODI - MEthodes et ingénierie des Langues, des Ontologies et du DIscours
IRIT - Institut de recherche en informatique de Toulouse
Abstract : Automatic construction of semantic resources at large scale usually relies on general purpose corpora as Wikipedia. This resource, by nature rich in encyclopedic knowledge, exposes part of this knowledge with strongly structured elements (infoboxes, categories, etc.). Several extractors have targeted these structures in order to enrich or to populate semantic resources as DBpedia, YAGO or BabelNet. The remain semi-structured textual structures, such as vertical enumerative structures (those using typographic and dispositional layout) have been however under-exploited. However, frequent in corpora, they are rich sources of specific semantic relations, such as hypernyms. This paper presents a distant learning approach for extracting hypernym relations from vertical enumerative structures of Wikipedia, with the aim of enriching DBpedia. Our relation extraction approach achieves an overall precision of 62%, and 99% of the extracted relations can enrich DBpedia, with respect to a reference corpus.
Keywords : Relation extraction
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Submitted on : Wednesday, April 3, 2019 - 3:52:20 PM
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  • HAL Id : hal-02089278, version 1
  • OATAO : 22670


Mouna Kamel, Cassia Trojahn. Towards Enriching DBpedia from Vertical Enumerative Structures Using a Distant Learning Approach. International Conference on Knowledge Engineering and Knowledge Management (EKAW 2018), Nov 2018, Nancy, France. pp.179-194. ⟨hal-02089278⟩



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