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Chapitre D'ouvrage Année : 2020

KEFT: Knowledge Extraction and Graph Building from Statistical Data Tables

Résumé

Data provided by statistical models are commonly represented by textual, tabular or graphical form in documents (reports, articles, posters and presentations). These documents are often available in PDF format. Even though it makes accessing a particular information more difficult, it is interesting to process the PDF documents directly. We present KEFT, a solution in the statistical domain and we describe the fully functional pipeline to constructing a knowledge graph by extracting entities and relations from statistical Data Tables. We showcase how this approach can be used to construct a knowledge graph from different statistical studies.
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Dates et versions

hal-03145214 , version 1 (18-02-2021)

Identifiants

Citer

Rabia Azzi, Sylvie Despres, Gayo Diallo. KEFT: Knowledge Extraction and Graph Building from Statistical Data Tables. Marcin Hernes; Krystian Wojtkiewicz; Edward Szczerbicki. Communications in Computer and Information Science, 1287, Springer, pp.701-713, 2020, CCIS - Communications in Computer and Information Science, ⟨10.1007/978-3-030-63119-2_57⟩. ⟨hal-03145214⟩
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