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'''Statistical relational learning''' ('''SRL''') is a subdiscipline of [[artificial intelligence]] and [[machine learning]] that is concerned with [[Domain model|___domain models]] that exhibit both [[uncertainty]] (which can be dealt with using statistical methods) and complex, [[relation (mathematics)|relational]] structure. 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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