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Line 2: ==Description== 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~~\|accessdate=16 October 2014~~}}</ref> Like MLNs PSL is a modelling language (with an accompanying implementation<ref>{{cite web\|url=https://github.com/linqs/psl\|accessdate=16 October 2014}}</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 integration of similarity functions in the into models. This is useful in problems such as [[Ontology Mapping]] and [[Entity Resolution]]. Also, in PSL the formula syntax is restricted to rules with conjunctive bodies. == See also ==

Probabilistic soft logic: Difference between revisions