Key Discovery for Numerical Data: Application to Oenological Practices

Abstract : The key discovery problem has been recently investigated for symbolical RDF data and tested on large datasets such as DBpedia and YAGO. The advantage of such methods is that they allow the automatic extraction of combinations of properties that uniquely identify every resource in a dataset (i.e., ontological rules). However, none of the existing approaches is able to treat real world numerical data. In this paper we propose a novel approach that allows to handle numerical RDF datasets for key discovery. We test the significance of our approach on the context of an oenological application and consider a wine dataset that represents the different chemical based flavourings. Discovering keys in this context contributes in the investigation of complementary flavors that allow to distinguish various wine sorts amongst themselves.
Type de document :
Communication dans un congrès
22. International Conference on Conceptual Structures : ICCS 2016, Jul 2016, Annecy, France. Springer International Publishing, Graph-Based Representation and Reasoning, 9717, pp.222-236, 2016, Lecture Notes in Computer Science. 〈https://www.irit.fr/ICCS2016/〉. 〈10.1007/978-3-319-40985-6_17〉
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Soumis le : jeudi 12 juillet 2018 - 20:14:07
Dernière modification le : vendredi 19 octobre 2018 - 15:18:02

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Danai Symeonidou, Isabelle Sanchez, Madalina Croitoru, Pascal Neveu, Nathalie Pernelle, et al.. Key Discovery for Numerical Data: Application to Oenological Practices. 22. International Conference on Conceptual Structures : ICCS 2016, Jul 2016, Annecy, France. Springer International Publishing, Graph-Based Representation and Reasoning, 9717, pp.222-236, 2016, Lecture Notes in Computer Science. 〈https://www.irit.fr/ICCS2016/〉. 〈10.1007/978-3-319-40985-6_17〉. 〈hal-01837440〉

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