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Communication Dans Un Congrès Année : 2018

Datil: Learning Fuzzy Ontology Datatypes

Résumé

Real-world applications using fuzzy ontologies are increasing in the last years, but the problem of fuzzy ontology learning has not received a lot of attention. While most of the previous approaches focus on the problem of learning fuzzy subclass axioms, we focus on learning fuzzy datatypes. In particular, we describe the Datil system, an implementation using unsupervised clustering algorithms to automatically obtain fuzzy datatypes from different input formats. We also illustrate the practical usefulness with an application: semantic lifestyle profiling.
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Dates et versions

hal-01951785 , version 1 (11-12-2018)

Identifiants

  • HAL Id : hal-01951785 , version 1

Citer

Ignacio Huitzil, Umberto Straccia, Natalia Díaz-Rodríguez, Fernando Bobillo. Datil: Learning Fuzzy Ontology Datatypes. IPMU 2018: 17th Information Processing and Management of Uncertainty in Knowledge-Based Systems Conference., Jun 2018, Cádiz, Spain. ⟨hal-01951785⟩
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