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Datil: Learning Fuzzy Ontology Datatypes

Ignacio Huitzil, Umberto Straccia, Natalia Diaz-Rodriguez, Fernando Bobillo, Datil: Learning Fuzzy Ontology Datatypes. In: David Pelta, B. Bouchon-Meunier, R. R. Yager (Eds.), Communications in Computer and Information Science (CCIS) , 1–12, Springer, 2018.


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
\emph{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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BibTeX entry:

  title = {Datil: Learning Fuzzy Ontology Datatypes},
  booktitle = {Communications in Computer and Information Science (CCIS) },
  author = {Huitzil, Ignacio and Straccia, Umberto and Diaz-Rodriguez, Natalia and Bobillo, Fernando},
  editor = {Pelta, David and Bouchon-Meunier, B. and Yager, R. R.},
  publisher = {Springer},
  pages = {1–12},
  year = {2018},
  keywords = {fuzzy ontologies; machine learning; lifestyle profiling},

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

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