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Statistical Relational Learning with Soft Quantifiers

Part of the Lecture Notes in Computer Science book series (LNAI,volume 9575)

Abstract

Quantification in statistical relational learning (SRL) is either existential or universal, however humans might be more inclined to express knowledge using soft quantifiers, such as “most” and “a few”. In this paper, we define the syntax and semantics of PSL\(^Q\), a new SRL framework that supports reasoning with soft quantifiers, and present its most probable explanation (MPE) inference algorithm. To the best of our knowledge, PSL\(^Q\) is the first SRL framework that combines soft quantifiers with first-order logic rules for modeling uncertain relational data. Our experimental results for link prediction in social trust networks demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves the accuracy of inferred results.

Keywords

  • Piecewise Linear Function
  • Link Prediction
  • Ground Atom
  • Trust Relation
  • Quantifier Mapping

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

  1. 1.

    Source code available at http://psl.umiacs.umd.edu.

  2. 2.

    www.epinions.com.

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Acknowledgements

We would like to thank the anonymous reviewers for their helpful comments and suggestions. This work was funded in part by the SBO-program of the Flemish Agency for Innovation by Science and Technology (IWT-SBO-Nr. 110067) and NSF grant IIS1218488. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF.

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Correspondence to Golnoosh Farnadi .

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Farnadi, G., Bach, S.H., Blondeel, M., Moens, MF., Getoor, L., De Cock, M. (2016). Statistical Relational Learning with Soft Quantifiers. In: Inoue, K., Ohwada, H., Yamamoto, A. (eds) Inductive Logic Programming. ILP 2015. Lecture Notes in Computer Science(), vol 9575. Springer, Cham. https://doi.org/10.1007/978-3-319-40566-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-40566-7_5

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