Learning probabilistic description logics: A framework and algorithms

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations

Abstract

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.
Original languageAmerican English
Title of host publicationLearning probabilistic description logics: A framework and algorithms
Pages28-39
Number of pages12
ISBN (Electronic)9783642253232
DOIs
StatePublished - 1 Jan 2011
Externally publishedYes
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 1 Jan 2018 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7094 LNAI
ISSN (Print)0302-9743

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period1/01/18 → …

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