Probabilistic soft logic

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 models over random variables with soft truth values from the interval [0,1].

Description

In recent years there has been a rise in the approaches that combine graphical models and first-order logic to allow the development of complex probabilistic models with relational structures. A notable example of such approaches is Markov logic networks (MLNs).[1] Like MLNs PSL is a modelling language (with an accompanying implementation[2]) 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

References

  1. ↑ Getoor, Lise; Taskar, Ben (12 Oct 2007). Introduction to Statistical Relational Learning. MIT Press. ISBN 0262072882.
  2. ↑ https://github.com/linqs/psl. Retrieved 16 October 2014. Missing or empty |title= (help)

External links

This article is issued from Wikipedia - version of the Thursday, March 03, 2016. The text is available under the Creative Commons Attribution/Share Alike but additional terms may apply for the media files.