Key Discovery for Numerical Data: Application to Oenological Practices

Danai Symeonidou (1) , Isabelle Sanchez (1) , Madalina Croitoru (2) , Pascal Neveu (1) , Nathalie Pernelle (3) , Fatiha Saïs (3) , Aurélie Roland-Vialaret (4) , Patrice Buche (5, 2) , Aunur Rofiq Muljarto (1) , Remi Schneider (4)

1 MISTEA - Mathématiques, Informatique et STatistique pour l'Environnement et l'Agronomie
2 GRAPHIK - Graphs for Inferences on Knowledge
3 LRI - Laboratoire de Recherche en Informatique
4 Nyseos
5 UMR IATE - Ingénierie des Agro-polymères et Technologies Émergentes

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.

Mots clés: ontology; lexical analysis

Mots clés: pratique oenologique; analyse lexicale; extraction terminologique; arôme; ontologie

Domaines: Intelligence artificielle [cs.AI]

Danai Symeonidou, Isabelle Sanchez, Madalina Croitoru, Pascal Neveu, Nathalie Pernelle, et al.. Key Discovery for Numerical Data: Application to Oenological Practices. ICCS: International Conference on Conceptual Structures, Jul 2016, Annecy, France. pp.222-236, ⟨10.1007/978-3-319-40985-6_17⟩. ⟨hal-01837440⟩