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

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.

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