PyPSL is a new library for building PSL models in Python.
This is still a prototype.
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.
To learn more about PSL, see this paper: Hinge-Loss Markov Random Fields and Probabilistic Soft Logic.
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User friendliness. PyPSL offers a consistent and user-friendly API.
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Easy extensibility. The modules composing the library are simple to extend.
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Work with Python. No separate models configuration files in a declarative format. Models are described in Python code, which is compact, easier to debug, and allows for ease of extensibility.
git clone [email protected]:br-g/pypsl.git
cd pypsl
make install
To get started, please follow these examples.