Learning sentences and assessments in probabilistic description logics

José Eduardo Ochoa Luna, Kate Revoredo, Fabio Gagliardi Cozman

Research output: Contribution to conferenceConference Paper

2 Scopus citations

Abstract

The representation of uncertainty in the semantic web can be eased by the use of learning techniques. To completely induce a probabilistic ontology (that is, an ontology encoded through a probabilistic description logic) from data, two basic tasks must be solved: (1) learning concept definitions and (2) learning probabilistic inclusions. In this paper we propose and test an algorithm that learns concept definitions using an inductive logic programming approach and then learns probabilistic inclusions using relational data.
Original languageAmerican English
Pages85-96
Number of pages12
StatePublished - 1 Dec 2010
Externally publishedYes
EventCEUR Workshop Proceedings -
Duration: 1 Jan 2016 → …

Conference

ConferenceCEUR Workshop Proceedings
Period1/01/16 → …

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