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Probabilistic soft logic (PSL) is a machine learning framework for developing probabilistic models. PSL models are easy to use and fast. You can define models using a straightforward logical syntax and solve them with fast convex optimization. PSL has produced state-of-the-art results in many areas spanning natural language processing, social-network analysis, knowledge graphs, recommender system, and computational biology. More information about PSL is available at the PSL homepage.

Getting Started with PSL

If you want to use PSL to build models, you probably do not need this source code. Instead, visit the Getting Started guide to learn how to create PSL projects that will automatically install a stable version of these libraries.

Installing PSL from Source

If you do want to install PSL from source, you can use Maven 3.x. In the top-level directory of the PSL source (which should be the same directory that holds this README), run:

mvn install

Citing PSL

We hope you find PSL useful! If you have, please consider citing PSL in any related publications as

  Author = {Bach, Stephen H. and Broecheler, Matthias and Huang, Bert and Getoor, Lise},
  Journal = {Journal of Machine Learning Research (JMLR)},
  Title = {Hinge-Loss {M}arkov Random Fields and Probabilistic Soft Logic},
  Year = {2017}

Additional Resources


The PSL software from the University of Maryland and the University of California Santa Cruz




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