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.
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Acknowledgments
The third author acknowledges the support of ANR grants ASPIQ (ANR-12-BS02-0003), QUALINCA (ANR-12-0012) and DURDUR (ANR-13-ALID-0002). The work of the third author has been carried out part of the research delegation at INRA MISTEA Montpellier and INRA IATE CEPIA Axe 5 Montpellier.
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Symeonidou, D. et al. (2016). Key Discovery for Numerical Data: Application to Oenological Practices. In: Haemmerlé, O., Stapleton, G., Faron Zucker, C. (eds) Graph-Based Representation and Reasoning. ICCS 2016. Lecture Notes in Computer Science(), vol 9717. Springer, Cham. https://doi.org/10.1007/978-3-319-40985-6_17
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DOI: https://doi.org/10.1007/978-3-319-40985-6_17
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