In this paper we present foxPSL, an extended and scalable implementation of Probabilistic Soft Logic (PSL) based on the distributed graph processing framework SIGNAL/COLLECT. 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-to-end 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 state-of-the-art implementation of ADMM consensus optimization in GraphLab, and show that foxPSL improves both inference time and solution quality.