Probabilistic soft logic
Probabilistic soft logic (PSL) is a SRL 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][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).[2] Like MLNs PSL is a modelling language (with an accompanying implementation[3]) 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 underlying inference to be solved quickly as a convex optimization problem. This is useful in problems such as collective classification, link prediction, social network modelling, and object identification/entity resolution/record linkage.
See also
References
- ^ Bach, Stephen; Broecheler, Matthias; Huang, Bert; Getoor, Lise (2017). "Hinge-Loss Markov Random Fields and Probabilistic Soft Logic". Journal of Machine Learning Research (JMLR). 18: 1–67.
- ^ Getoor, Lise; Taskar, Ben (12 Oct 2007). Introduction to Statistical Relational Learning. MIT Press. ISBN 0262072882.
- ^ "GitHub repository". Retrieved 26 March 2018.