'''Probabilistic soft logic (PSL)''' is a framework for collective, probabilistic reasoning in relational domains. PSL uses [[first order logic]] rules as a template language for [[graphical modelsmodel]]s over [[random variablesvariable]]s with soft truth values from the interval [0;,1].
==Description==
In recent years there has been a rise in the approaches that combine [[graphical modelsmodel]]s and [[first-order logic]] to allow the development of complex probabilistic models with relational structures. A notable is example of such approaches is [[Markov Logiclogic Networksnetwork]]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 a 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 predicatepredicates in an interval between [0,1]. This allows for the integration of similarity functions in the into models. This is useful in problems such [[Ontology Mapping]] and [[Entity Resolution]]. Also, in PSL the formula syntax is restricted to rules with conjunctive bodies.
