KEFT: Knowledge Extraction and Graph Building from Statistical Data Tables
| dc.rights.license | open | en_US |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | AZZI, Rabia | |
| dc.contributor.author | DESPRES, Sylvie | |
| hal.structure.identifier | Statistics In System biology and Translational Medicine [SISTM] | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | DIALLO, Abdourahmane Gayo
IDREF: 112800084 | |
| dc.contributor.editor | Marcin Hernes | |
| dc.contributor.editor | Krystian Wojtkiewicz | |
| dc.contributor.editor | Edward Szczerbicki | |
| dc.date.accessioned | 2021-05-07T08:42:06Z | |
| dc.date.available | 2021-05-07T08:42:06Z | |
| dc.date.issued | 2020-11-19 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/27187 | |
| dc.description.abstractEn | 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. | |
| dc.language.iso | EN | en_US |
| dc.publisher | Springer | en_US |
| dc.source.title | Communications in Computer and Information Science | en_US |
| dc.subject.en | Information extraction | |
| dc.subject.en | Knowledge graph | |
| dc.subject.en | Table recognition | |
| dc.subject.en | PDF document | |
| dc.title.en | KEFT: Knowledge Extraction and Graph Building from Statistical Data Tables | |
| dc.type | Chapitre d'ouvrage | en_US |
| dc.identifier.doi | 10.1007/978-3-030-63119-2_57 | en_US |
| dc.subject.hal | Informatique [cs]/Intelligence artificielle [cs.AI] | en_US |
| bordeaux.page | 701-713 | en_US |
| bordeaux.volume | 1287 | en_US |
| bordeaux.hal.laboratories | Bordeaux Population Health Research Center (BPH) - U1219 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | INSERM | en_US |
| bordeaux.team | ERIAS | en_US |
| bordeaux.team | SISTM_BPH | |
| bordeaux.team | SISTM | en_US |
| bordeaux.inpress | non | en_US |
| bordeaux.import.source | hal | |
| hal.identifier | hal-03145214 | |
| hal.version | 1 | |
| hal.export | false | |
| workflow.import.source | hal | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=Communications%20in%20Computer%20and%20Information%20Science&rft.date=2020-11-19&rft.volume=1287&rft.spage=701-713&rft.epage=701-713&rft.au=AZZI,%20Rabia&DESPRES,%20Sylvie&DIALLO,%20Abdourahmane%20Gayo&rft.genre=unknown |
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