Soft quantification in statistical relational learning

We present a new statistical relational learning (SRL) framework that supports reasoning with soft quantifiers, such as “most” and “a few.” We define the syntax and the semantics of this language, which we call $$\hbox {PSL}^Q$$PSLQ, and present a most probable explanation inference algorithm for it. To the best of our knowledge, $$\hbox {PSL}^Q$$PSLQ is the first SRL framework that combines soft quantifiers with first-order logic rules for modelling uncertain relational data. Our experimental results for two real-world applications, link prediction in social trust networks and user profiling in social networks, demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves inference accuracy.

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