Probabilistic soft logic

Probabilistic Soft Logic (PSL) is a 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 the strengths of two powerful theories – 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 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-hard) combinatorial one.

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). 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.

See also

• Statistical relational learning
• Probabilistic logic network
• Markov logic network
• Fuzzy logic

External links

• Probabilistic soft logic main web page
• Official PSL implementation
• A list of publications about PSL
• Video lectures about PSL on Youtube