Proceedings of the third annual conference on Autonomous Agents
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Adaptive duplicate detection using learnable string similarity measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Exploiting shared correlations in probabilistic databases
Proceedings of the VLDB Endowment
Joint inference in information extraction
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Lifted probabilistic inference with counting formulas
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
A general method for reducing the complexity of relational inference and its application to MCMC
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Lifted first-order belief propagation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Lifted first-order probabilistic inference
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Compiling relational Bayesian networks for exact inference
International Journal of Approximate Reasoning
Markov Logic: An Interface Layer for Artificial Intelligence
Markov Logic: An Interface Layer for Artificial Intelligence
Speeding up inference in statistical relational learning by clustering similar query literals
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Generative Structure Learning for Markov Logic Networks
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Tuffy: scaling up statistical inference in Markov logic networks using an RDBMS
Proceedings of the VLDB Endowment
Pruning search space for weighted first order horn clause satisfiability
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Probabilistic-logical web data integration
RW'11 Proceedings of the 7th international conference on Reasoning web: semantic technologies for the web of data
Constraint propagation for efficient inference in Markov logic
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Programming with personalized pagerank: a locally groundable first-order probabilistic logic
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Statistical relational data integration for information extraction
RW'13 Proceedings of the 9th international conference on Reasoning Web: semantic technologies for intelligent data access
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Statistical-relational reasoning has received much attention due to its ability to robustly model complex relationships. A key challenge is tractable inference, especially in domains involving many objects, due to the combinatorics involved. One can accelerate inference by using approximation techniques, "lazy" algorithms, etc. We consider Markov Logic Networks (MLNs), which involve counting how often logical formulae are satisfied. We propose a preprocessing algorithm that can substantially reduce the effective size of MLNs by rapidly counting how often the evidence satisfies each formula, regardless of the truth values of the query literals. This is a general preprocessing method that loses no information and can be used for any MLN inference algorithm. We evaluate our algorithm empirically in three real-world domains, greatly reducing the work needed during subsequent inference. Such reduction might even allow exact inference to be performed when sampling methods would be otherwise necessary.