DAGOBAH: Annotation sémantique de données tabulaires par comparaison du contexte des tables et d’un graphe de connaissances

Huynh, Viet-Phi; Liu1, Jixiong; Chabot, Yoan; Deuzé, Frédéric; Labbé, Thomas; Monnin, Pierre; Troncy, Raphaël
CNIA 2022, Conférence Nationale en Intelligence Artificielle, 27-29 June 2022, Saint-Etienne, France

In this paper, we present the latest improvements of the DAGOBAH system that performs automatic pre-processing and semantic interpretation of tables. In particular, we report promising results obtained in the SemTab 2021 challenge thanks to optimisations in lookup mechanisms and new techniques for studying the context of nodes in the target knowledge graph. We also present the deployment of DAGOBAH algorithms within the Orange company via the TableAnnotation API and a front-end DAGOBAH user interface. These two access methods enable to accelerate the adoption of Semantic Table Interpretation solutions within the company to meet industrial needs. 


HAL
Type:
Conference
City:
Saint-Etienne
Date:
2022-06-29
Department:
Data Science
Eurecom Ref:
6955
Copyright:
© EURECOM. Personal use of this material is permitted. The definitive version of this paper was published in CNIA 2022, Conférence Nationale en Intelligence Artificielle, 27-29 June 2022, Saint-Etienne, France and is available at :
See also:

PERMALINK : https://www.eurecom.fr/publication/6955