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Soft quantification in statistical relational learning
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DOI：10.1007/s10994-017-5647-3.png)
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Abstract
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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 , and present a most probable explanation inference algorithm for it. To the best of our knowledge, 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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