Probabilistic soft logic: Difference between revisions

Revision as of 21:55, 12 May 2017

Revision as of 21:55, 12 May 2017

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}}</ref> Like MLNs PSL is a modelling language (with an accompanying implementation<ref>{{cite web|url=https://github.com/linqs/psltitlepsl|title=GitHub repository||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 ==