LINQS

STATISTICAL RELATIONAL LEARNING GROUP @ UMD



 

Soft quantification in statistical relational learning

Golnoosh Farnadi, Stephen H. Bach, Marie-Francine Moens, Lise Getoor, Martine De Cock
Machine Learning Journal - 2017
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 PSLQ, and present a most probable explanation inference algorithm for it. To the best of our knowledge, 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.

BibTex references

@Article{farnadi:mlj17,
  author       = "Farnadi, Golnoosh and Bach, Stephen H. and Moens, Marie-Francine and Getoor, Lise and De Cock, Martine",
  title        = "Soft quantification in statistical relational learning",
  journal      = "Machine Learning Journal",
  year         = "2017",
}

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