FoxPSL: An Extended and Scalable PSL Implementation
Sara Magliacane, Philip Stutz, Paul Thomas Groth, Abraham Bernstein · VU Research Portal · 2015
In this paper we present foxPSL, an extended and scalable implementation of Probabilistic Soft Logic (PSL) based on the distributed graph processing framework SIGNAL/COL-LECT.PSL is a template language for hinge-loss Markov Random Fields, in which MAP inference is formulated as a constrained convex minimization problem.A key feature of PSL is the capability to represent soft truth values, allowing the expression of complex domain knowledge.To the best of our knowledge, foxPSL is the first end-toend distributed PSL implementation, supporting the full PSL pipeline from problem definition to a distributed solver that implements the Alternating Direction Method of Multipliers (ADMM) consensus optimization.foxPSL provides a Domain Specific Language that extends standard PSL with a type system and existential quantifiers, allowing for efficient grounding.We compare the performance of foxPSL to a stateof-the-art implementation of ADMM consensus optimization in GraphLab, and show that foxPSL improves both inference time and solution quality.