KEFT: Knowledge Extraction and Graph Building from Statistical Data Tables
DIALLO, Abdourahmane Gayo
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
DIALLO, Abdourahmane Gayo
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
< Réduire
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
Langue
EN
Chapitre d'ouvrage
Ce document a été publié dans
Communications in Computer and Information Science. 2020-11-19, vol. 1287, p. 701-713
Springer
Résumé en anglais
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 ...Lire la suite >
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.< Réduire
Mots clés en anglais
Information extraction
Knowledge graph
Table recognition
PDF document
Unités de recherche