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.

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.

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

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

@article{bach:jmlr17,
  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}
}

• PSL Homepage
• PSL Examples
• API Reference
• PSL Source Repository
• PSL Wiki
• Getting Started Guide
• User Group