Ontology-Aware Prediction from Rules: A Reconciliation-Based Approach

Fatiha Saïs 1 Rallou Thomopoulos 2
2 GRAPHIK - Graphs for Inferences on Knowledge
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Our work is related to the general problem of constructing predictions for decision support issues. It relies on knowledge expressed by numerous rules with homogeneous structure, extracted from various scientific publications in a specific domain. We propose a predictive approach that takes two stages: a reconciliation stage which identifies groups of rules expressing a common experimental tendency and a prediction stage which generates new rules, using both descriptions coming from experimental conditions and groups of reconciled rules obtained in stage one. The method has been tested with a case study related to food science and it has been compared to a classical approach based on decision trees. The results are promising in terms of accuracy, completeness and error rate.
Type de document :
Article dans une revue
Knowledge-Based Systems, Elsevier, 2014, 67, pp.117-130. 〈10.1016/j.knosys.2014.05.023〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01092431
Contributeur : Rallou Thomopoulos <>
Soumis le : lundi 8 décembre 2014 - 16:36:16
Dernière modification le : jeudi 24 mai 2018 - 15:59:22

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Fatiha Saïs, Rallou Thomopoulos. Ontology-Aware Prediction from Rules: A Reconciliation-Based Approach. Knowledge-Based Systems, Elsevier, 2014, 67, pp.117-130. 〈10.1016/j.knosys.2014.05.023〉. 〈lirmm-01092431〉

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