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Type | Conference or Workshop Paper |
Scope | Discipline-based scholarship |
Published in Proceedings | Yes |
Title | Wolf: An extended and scalable PSL implementation |
Organization Unit | |
Authors |
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Presentation Type | paper |
Item Subtype | Original Work |
Refereed | Yes |
Status | Published in final form |
Language |
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Event Title | AAAI Spring Symposium on Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches |
Event Type | conference |
Event Location | Stanford University, CA |
Event Start Date | March 23 - 2015 |
Event End Date | March 25 - 2015 |
Series Name | AAAI Spring Symposium |
Place of Publication | Palo Alto, California |
Publisher | AAAI Press |
Abstract Text | 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. |
Official URL | https://sites.google.com/site/krr2015/home/accepted-papers |
Other Identification Number | merlin-id:11653 |
PDF File | Download from ZORA |
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