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{{short description|Subdiscipline of artificial intelligence}}
'''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=getoor:book07 /><ref name=rossi:jair12 />
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 name=getoor:book07 />▼
▲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.
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).
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* [[Fuzzy logic]]
* [[Grammar induction]]
* [[Knowledge graph embedding]]
== Resources ==
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* Hassan Khosravi and Bahareh Bina: ''[http://www.cs.ubc.ca/~hkhosrav/pub/survey.pdf A Survey on Statistical Relational Learning]'', Advances in Artificial Intelligence, Lecture Notes in Computer Science, Volume 6085/2010, 256–268, Springer, 2010
* 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, page 363-441, 2012
*
== References ==
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