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Wolf: An extended and scalable PSL implementation


Magliacane, Sara; Stutz, Philip; Groth, Paul; Bernstein, Abraham (2015). Wolf: An extended and scalable PSL implementation. In: AAAI Spring Symposium on Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches, Stanford University, CA, 23 March 2015 - 25 March 2015.

Abstract

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.

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.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:25 March 2015
Deposited On:07 Jul 2015 12:15
Last Modified:05 Apr 2016 19:17
Publisher:AAAI Press
Series Name:AAAI Spring Symposium
Official URL:https://sites.google.com/site/krr2015/home/accepted-papers
Other Identification Number:merlin-id:11653
Permanent URL: https://doi.org/10.5167/uzh-111244

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