Statistical relational learning: Difference between revisions

Revision as of 21:59, 28 October 2019

Revision as of 14:59, 29 April 2020

Line 1:

'''Statistical relational learning''' ('''SRL''') is a subdiscipline of [[artificial intelligence]] and [[machine learning]] that is concerned with [[___domain model]]s that exhibit both [[uncertainty]] (which can be dealt with using statistical methods) and complex, [[relation (mathematics)|relational]] structure.<ref name=Getoor2007>[[Lise Getoor]] and [[Ben Taskar]]: ''[https://books.google.com/books?id=lSkIewOw2WoC&printsec=frontcover#v=onepage&q&f=false Introduction to statistical relational learning]'', MIT Press, 2007</ref><ref>Ryan A. Rossi, Luke K. McDowell, David W. Aha, and Jennifer Neville, "[http://www.jair.org/media/3659/live-3659-6589-jair.pdf Transforming Graph Data for Statistical Relational Learning.]" ''Journal of Artificial Intelligence Research (JAIR)'', '''Volume 45''' (2012), pp. 363-441.</ref> Note that SRL is sometimes called Relational Machine Learning (RML) in the literature. Typically, the [[knowledge representation]] formalisms developed in SRL use (a subset of) [[first-order logic]] to describe relational properties of a ___domain in a general manner ([[universal quantification]]) and draw upon [[probabilistic graphical model]]s (such as [[Bayesian network]]s or [[Markov network]]s) to model the uncertainty; some also build upon the methods of [[inductive logic programming]]. Significant contributions to the field have been made since the late 1990s.<ref>{{citation_needed|date=December 2016}}</ref>

As is evident from the characterization above, the field is not strictly limited to learning aspects; it is equally concerned with [[Semantic_reasoner|reasoning]] (specifically [[statistical inference|probabilistic inference]]) and [[knowledge representation]]. Therefore, alternative terms that reflect the main foci of the field include ''statistical relational learning and reasoning'' (emphasizing the importance of reasoning) and ''first-order probabilistic languages'' (emphasizing the key properties of the languages with which models are represented).

== Canonical tasks ==