Learning probabilistic description logics: A framework and algorithms

José Eduardo Ochoa-Luna, Kate Revoredo, Fábio Gagliardi Cozman

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferencia

17 Citas (Scopus)

Resumen

Description logics have become a prominent paradigm in knowledge representation (particularly for the Semantic Web), but they typically do not include explicit representation of uncertainty. In this paper, we propose a framework for automatically learning a Probabilistic Description Logic from data. We argue that one must learn both concept definitions and probabilistic assignments. We also propose algorithms that do so and evaluate these algorithms on real data. © 2011 Springer-Verlag.
Idioma originalInglés estadounidense
Título de la publicación alojadaLearning probabilistic description logics: A framework and algorithms
Páginas28-39
Número de páginas12
ISBN (versión digital)9783642253232
DOI
EstadoPublicada - 1 ene 2011
Publicado de forma externa
EventoLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duración: 1 ene 2018 → …

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen7094 LNAI
ISSN (versión impresa)0302-9743

Conferencia

ConferenciaLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Período1/01/18 → …

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    Ochoa-Luna, J. E., Revoredo, K., & Cozman, F. G. (2011). Learning probabilistic description logics: A framework and algorithms. En Learning probabilistic description logics: A framework and algorithms (pp. 28-39). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7094 LNAI). https://doi.org/10.1007/978-3-642-25324-9_3