Semi-automatic loading of a microbial risk in food database thanks to an ontology in the context of linked data

Abstract : A preliminary step in microbial risk assessment in food is to gather and to capitalize experimental data in order to feed simulation models. We have designed, in the predictive modeling platform Sym'Previus (www.symprevius.org), a relational database, MicroRisk-RDB [1], to store experimental data in microbial risk. Data capitalization task encounters a challenging lock. Indeed, original data are spread out in heterogeneous data sources (scientific papers, technical reports / sheets, PhD thesis ...). Moreover, they are expressed in heterogeneous formats (mainly tables, text and graphics) and vocabularies. Manual entering of data in a database (e.g. MicroRisk-RDB) is therefore a time-consuming task. We present in this paper methods and tools to facilitate data capitalization and reusability in the framework of the Linked Data initiative , which promotes the publication and connection of structured data on the Web.
Type de document :
Communication dans un congrès
ICPMF 2013 - 8th International Conference on Predictive Modelling in Food, Sep 2013, Paris, France. 2013
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https://hal.archives-ouvertes.fr/hal-01123270
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Soumis le : mercredi 4 mars 2015 - 20:38:25
Dernière modification le : jeudi 28 février 2019 - 17:43:31

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  • HAL Id : hal-01123270, version 1
  • PRODINRA : 279668

Citation

Patrice Buche, Stephane Dervaux, Juliette Dibie, Mariana Ibanescu, Thomas Hecht, et al.. Semi-automatic loading of a microbial risk in food database thanks to an ontology in the context of linked data. ICPMF 2013 - 8th International Conference on Predictive Modelling in Food, Sep 2013, Paris, France. 2013. 〈hal-01123270〉

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