DAGOBAH: Table and graph contexts for efficient semantic annotation of tabular data

Huynh, Viet-Phi; Liu, Jixiong; Chabot, Yoan; Deuzé, Frédéric; Labbé, Thomas; Monnin, Pierre; Troncy, Raphaël
SemTab 2021, Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, co-located with the 20th International Semantic Web Conference (ISWC 2021), 27 October 2021 (Virtual conference)

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.

Data Science
Eurecom Ref:

PERMALINK : https://www.eurecom.fr/publication/6842