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'''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 [[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).
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== Resources ==
* Brian Milch, and Stuart J. Russell: ''[ftp://nozdr.ru/biblio/kolxo3/Cs/CsLn/Inductive%20Logic%20Programming,%2016%20conf.,%20ILP%202006(LNCS4455,%20Springer,%202006)(ISBN%203540738460)(466s).pdf#page=20 First-Order Probabilistic Languages: Into the Unknown]'', Inductive Logic Programming, volume 4455 of [[Lecture Notes in Computer Science]], page 10–24. Springer, 2006
* Rodrigo de Salvo Braz, Eyal Amir, and Dan Roth: ''[http://www.ai.sri.com/~braz/papers/sci-chapter.pdf A Survey of First-Order Probabilistic Models]'', Innovations in Bayesian Networks, volume 156 of Studies in Computational Intelligence, Springer, 2008
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