Soft quantification in statistical relational learning

• Published 2017 in Machine Learning

Abstract

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 $$\hbox {PSL}^Q$$ PSL Q , and present a most probable explanation inference algorithm for it. To the best of our knowledge, $$\hbox {PSL}^Q$$ PSL Q 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.

DOI: 10.1007/s10994-017-5647-3

Cite this paper

@article{Farnadi2017SoftQI, title={Soft quantification in statistical relational learning}, author={Golnoosh Farnadi and Stephen H. Bach and Marie-Francine Moens and Lise Getoor and Martine De Cock}, journal={Machine Learning}, year={2017}, pages={1-21} }