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

Article type: Research Article

Authors: Bellodi, Elena^{; *}

Affiliations: Department of Engineering, University of Ferrara, Via Saragat, Ferrara, Italy

Correspondence: [*] Corresponding author: Elena Bellodi, Department of Engineering, University of Ferrara, Via Saragat 1, 44122 Ferrara, Italy. Tel.: +390532974882; E-mail: elena.bellodi@unife.it.

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.

Keywords: Statistical relational learning, distribution semantics, probabilistic logic programming, probabilistic description logics

DOI: 10.3233/IA-221072

Journal: Intelligenza Artificiale, vol. 17, no. 1, pp. 143-156, 2023