'''Statistical relational learning (SRL)''' is a subdiscipline of [[artificial intelligence]] and [[machine learning]] that is concerned with models of [[Domain model|domains___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]].<ref>{{cite journal|last1=Nassif|first1=Houssam|last2=Kuusisto|first2=Finn|last3=Burnside|first3=Elizabeth S|last4=Page|first4=David|last5=Shavlik|first5=Jude|last6=Santos Costa|first6=Vitor|title=Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling|journal=European Conference on Machine Learning (ECML'13)|date=2013|pages=595–611|url=http://pages.cs.wisc.edu/~hous21/papers/ECML13.pdf|___location=Prague}}</ref> 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 [[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).
