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Datil: Learning Fuzzy Ontology Datatypes

I Huitzil, U Straccia, N Díaz-Rodríguez, F Bobillo, Datil: Learning Fuzzy Ontology Datatypes. In: J. Medina, M. Ojeda-Aciego, J.L. Verdegay, I. Perfilieva, B. Bouchon-Meunier, R.R Yager (Eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications, Communications in Computer and Information Science 854, 100–112, Springer, 2018.

http://dx.doi.org/10.1007/978-3-319-91476-3

Abstract:

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 di fferent input formats. We also illustrate the practical usefulness with an application: semantic lifestyle pro filing.

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BibTeX entry:

@INPROCEEDINGS{inpHuStDxBo18a,
  title = {Datil: Learning Fuzzy Ontology Datatypes},
  booktitle = { Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications},
  author = {Huitzil, I and Straccia, U and Díaz-Rodríguez, N and Bobillo, F},
  volume = {854},
  series = {Communications in Computer and Information Science},
  editor = {Medina, J. and Ojeda-Aciego, M. and Verdegay, J.L. and Perfilieva, I. and Bouchon-Meunier, B. and Yager, R.R},
  publisher = {Springer},
  pages = {100–112},
  year = {2018},
  keywords = {fuzzy ontologies; machine learning; unsupervised learning; lifestyle pro ling;},
  ISSN = {1865-0929},
}

Belongs to TUCS Research Unit(s): Embedded Systems Laboratory (ESLAB)

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