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Concept
Probabilistic soft logic
Résumé
Probabilistic Soft Logic (PSL) is a statistical relational learning (SRL) framework for modeling probabilistic and relational domains.
It is applicable to a variety of machine learning problems, such as collective classification, entity resolution, link prediction, and ontology alignment.
PSL combines two tools: first-order logic, with its ability to succinctly represent complex phenomena, and probabilistic graphical models, which capture the uncertainty and incompleteness inherent in real-world knowledge.
More specifically, PSL uses "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 maximum a posteriori (MAP) state).
The "softening" of the logical formulas makes inference a polynomial time operation rather than an NP-hard operation.
The SRL community has introduced multiple 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).
Like MLNs, PSL is a modelling language (with an accompanying implementation) 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.
Probabilistic Soft Logic was first released in 2009 by Lise Getoor and Matthias Broecheler.
This first version focused heavily on reasoning about similarities between entities.
Later versions of PSL would still keep the ability to reason about similarities, but generalize the language to be more expressive.
In 2017, a Journal of Machine Learning Research article detailing PSL and the underlying graphical model was published along with the release of a new major version of PSL (2.
Source officielle
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