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[disambiguation needed] and entity resolution. Also, in PSL the formula syntax is restricted to rules with conjunctive bodies.
See also
References
- ^ Getoor, Lise; Taskar, Ben (12 Oct 2007). Introduction to Statistical Relational Learning. MIT Press. ISBN 0262072882.
- ^ "GitHub repository". Retrieved 16 October 2014.