The distribution semantics in probabilistic logic programming and probabilistic description logics: a survey-Reference-Cited by-同舟云学术

The distribution semantics in probabilistic logic programming and probabilistic description logics: a survey

Published:2023-06-07 Issue:1 Volume:17 Page:143-156
ISSN:1724-8035
Container-title:Intelligenza Artificiale
language:
Short-container-title:IA

Author:

Bellodi Elena¹

Affiliation:

1. Department of Engineering, University of Ferrara, Via Saragat, Ferrara, Italy

Abstract

Representing uncertain information is crucial for modeling real world domains. This has been fully recognized both in the field of Logic Programming and of Description Logics (DLs), with the introduction of probabilistic logic languages and various probabilistic extensions of DLs respectively. Several works have considered the distribution semantics as the underlying semantics of Probabilistic Logic Programming (PLP) languages and probabilistic DLs (PDLs), and have then targeted the problem of reasoning and learning in them. This paper is a survey of inference, parameter and structure learning algorithms for PLP languages and PDLs based on the distribution semantics. A few of these algorithms are also available as web applications.

Publisher

IOS Press

Subject

Artificial Intelligence

Reference48 articles.

1. Axiom pinpointing in generaltableaux;Baader;Journal of Logic and Computation,2010

2. Baader F. , Calvanese D. , McGuinness D.L. , Nardi D. and Patel-Schneider P.F. , editors. The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, New York, NY, USA, 2003.

3. Baader F. and Nutt W. , Basic description logics. In Description Logic Handbook, Cambridge University Press, 2002, p. 47100.

4. leanTAP: Lean tableau-based deduction;Beckert;Journal of Automated Reasoning,1995

5. Map inference forprobabilistic logic programming;Bellodi;Theory and Practice of LogicProgramming,2020