Making Ontology-Based Knowledge and Decision Trees interact: an Approach to Enrich Knowledge and Increase Expert Confidence in Data-Driven Models

Abstract : When using data-driven models to make simulations and predictions in experimental sciences, it is essential for the domain expert to be confident about the predicted values. Increasing this confidence can be done by using interpretable models, so that the expert can follow the model reasoning pattern, and by integrating expert knowledge to the model itself. New pieces of useful formalised knowledge can then be integrated to an existing corpus while data-driven models are tuned according to the expert advice. In this paper, we propose a generic interactive procedure, relying on an ontology to model qualitative knowledge and on decision trees as a data-driven rule learning method. A case study based on data issued from multiple scientific papers in the field of cereal transformation illustrates the approach.
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Conference papers
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00538795
Contributor : Rallou Thomopoulos <>
Submitted on : Tuesday, November 23, 2010 - 12:37:20 PM
Last modification on : Friday, March 29, 2019 - 9:12:10 AM

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  • HAL Id : lirmm-00538795, version 1

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Iyan Johnson, Joel Abecassis, Brigitte Charnomordic, Sébastien Destercke, Rallou Thomopoulos. Making Ontology-Based Knowledge and Decision Trees interact: an Approach to Enrich Knowledge and Increase Expert Confidence in Data-Driven Models. KSEM: Knowledge Science, Engineering and Management, 2010, Belfast, United Kingdom. pp.304-316. ⟨lirmm-00538795⟩

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