A decision support system using multi-source scientific data, an ontological approach and soft computing - application to eco-efficient biorefinery

Abstract : In decision tasks such as bioprocess efficiency comparison, scientific literature is a valuable source of data. This large number of scientific data is heterogeneously structured, mainly in textual format. Innovative tools able to integrate and treat constantly new information are required. In this context, the use of semantic web methods such as ontologies seems relevant to structure the experimental information. Imprecision and uncertainty can arise from data incompleteness and variability. This is particularly true for processes involving biological materials. Document reliability should also be considered. Soft computing methods have the potential to be the kingpin of specialized software that can be integrated in decision support systems (DSS) intended to solve these issues. This paper presents the implementation of a pipeline which permits to: (1) structure and integrate the experimental data of interest by using ontologies, (2) assess data source reliability, (3) compute and visualize indicators taking into account data imprecision.
Type de document :
Communication dans un congrès
IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016) , Jul 2016, Vancouver, Canada. pp.249-256, 2016, 〈10.1109/FUZZ-IEEE.2016.7737694〉
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Dernière modification le : jeudi 24 mai 2018 - 15:59:22
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Charlotte Lousteau-Cazalet, Abdellatif Barakat, Jean-Pierre Belaud, Patrice Buche, Brigitte Charnomordic, et al.. A decision support system using multi-source scientific data, an ontological approach and soft computing - application to eco-efficient biorefinery. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016) , Jul 2016, Vancouver, Canada. pp.249-256, 2016, 〈10.1109/FUZZ-IEEE.2016.7737694〉. 〈lirmm-01410515〉

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