Probabilistic soft logic: Difference between revisions

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'''Probabilistic softSoft logicLogic (PSL)''' is a [[Statistical relational learning | SRL]] framework for collective,modeling [[probabilistic reasoning]] inand relational domains.

PSL uses [[first order logic]] rules as a template language for [[graphical model]]s over [[random variable]]s with soft truth values from the interval&nbsp;[0,1].<ref>{{cite journal |last1=Bach |first1=Stephen |last2=Broecheler |first2=Matthias |last3=Huang |first3=Bert |last4=Getoor |first4=Lise |date=2017 |title=Hinge-Loss Markov Random Fields and Probabilistic Soft Logic |journal=Journal of Machine Learning Research |volume=18 |pages=1–67}}</ref>.

It is applicable to a variety of [[machine learning]] problems, such as [[collective classification]], [[Record linkage | entity resolution]], [[link prediction]], and [[ontology alignment]].

PSL combines the strengths of two powerful theories – [[first-order logic]], with its ability to succinctly represent complex phenomena, and [[graphical model | probabilistic graphical models]], which capture the uncertainty and incompleteness inherent in real-world knowledge.

More specifically, PSL uses [[Fuzzy logic | "soft" logic]] as its logical component and [[Markov random fields]] as its statistical model.

PSL provides sophisticated inference techniques for finding the most likely answer (i.e. the MAP state).

The "softening" of the logical formulas allows us to cast the inference problem as a polynomial-time optimization, rather than a (much more difficult [[NP-hardness | NP-hard]]) combinatorial one.

== 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>