Decision support tool for the agri-food sector using data annotated by ontology and Bayesian network: a proof of concept applied to milk microfiltration.
Résumé
The scientific literature is a valuable source of information for developing predictive models to design
decision support systems. However, scientific data are heterogeneously structured expressed using
different vocabularies. This study developed a generic workflow that combines ontology, databases,
and computer calculation tools based on the theory of belief functions and Bayesian networks. The
ontology paradigm is used to help integrate data from heterogeneous sources. Bayesian network
is estimated using the integrated data taking into account their reliability. The proposed method is
unique in the sense that it proposes an annotation and reasoning tool dedicated to systematic analysis
of the literature, which takes into account expert knowledge of the domain at several levels: ontology
definition, reliability criteria, and dependence relations between variables in the BN. The workflow
is assessed successfully by applying it to a complex food engineering process: skimmed milk
microfiltration. It represents an original contribution to the state of the art in this application domain.
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