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Conference Papers Year : 2021

A Framework for Automatically Interpreting Tabular Data at Orange

Yoan Chabot
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  • PersonId : 1268049
Pierre Monnin
Pierre Monnin
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Viet-Phi Huynh
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Thomas Labbé
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Raphaël Troncy


Large parts of knowledge of companies are encoded in tabular data. Being able to interpret such data is key to increase business efficiency and to propose innovative services, and Orange is no exception. With more than 140,000 employees worldwide and a heterogeneous client portfolio, Orange produces a phenomenal amount of tabular data every day. These tables are viscerally embedded in internal services and products (e.g., network logs, multimedia catalogs). Hence, they are a source to discover new knowledge. Although this encourages the development of efficient tools to process them, several issues are negatively impacting their use. First, the volume curse makes difficult to identify the right dataset for a given use case. Then, the knowledge gap between data producers/consumers is exacerbated by our language footprint (seven main languages), the heterogeneous tools producing various table formats, and the experience/jobs of employees leading to similar concepts being expressed by different terms across tables.
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hal-04170860 , version 1 (25-07-2023)


  • HAL Id : hal-04170860 , version 1


Yoan Chabot, Pierre Monnin, Pierre Monnin, Frédéric Deuzé, Viet-Phi Huynh, et al.. A Framework for Automatically Interpreting Tabular Data at Orange. The 20th International Semantic Web Conference (ISWC 2021), Oct 2021, En ligne, Unknown Region. pp.413. ⟨hal-04170860⟩


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