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In recent years there has been a rise in the approaches that combine [[graphical model]]s and [[first-order logic]] to allow the development of complex probabilistic models with relational structures.
A notable example of such approaches is [[Markov logic network]]s (MLNs).<ref>{{cite book|last1=Getoor|first1=Lise|last2=Taskar|first2=Ben|title=Introduction to Statistical Relational Learning|date=12 Oct 2007|publisher=MIT Press|isbn=0262072882}}</ref>
Like MLNs, PSL is a modelling language (with an accompanying implementation<ref>{{cite web|url=https://github.com/linqs/psl|title=GitHub repository|accessdate=26 March 2018}}</ref>) for learning and predicting in relational domains.
Unlike MLNs, PSL uses soft truth values for predicates in an interval between [0,1].
This allows for the underlying inference to be solved quickly as a convex optimization problem.
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