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]
< Reduce

Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
Language
EN
Chapitre d'ouvrage
This item was published in
Communications in Computer and Information Science. 2020-11-19, vol. 1287, p. 701-713
Springer
English Abstract
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 ...Read more >
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.Read less <
English Keywords
Information extraction
Knowledge graph
Table recognition
PDF document