Markov logic networks |
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Authors: | Matthew Richardson Pedro Domingos |
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Affiliation: | (1) Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195-2350, USA |
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Abstract: | We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation.
A Markov logic network (MLN) is a first-order knowledge base with a weight attached to each formula (or clause). Together
with a set of constants representing objects in the domain, it specifies a ground Markov network containing one feature for
each possible grounding of a first-order formula in the KB, with the corresponding weight. Inference in MLNs is performed
by MCMC over the minimal subset of the ground network required for answering the query. Weights are efficiently learned from
relational databases by iteratively optimizing a pseudo-likelihood measure. Optionally, additional clauses are learned using
inductive logic programming techniques. Experiments with a real-world database and knowledge base in a university domain illustrate
the promise of this approach.
Editors: Hendrik Blockeel, David Jensen and Stefan Kramer
An erratum to this article is available at . |
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Keywords: | Statistical relational learning Markov networks Markov random fields Log-linear models Graphical models First-order logic Satisfiability Inductive logic programming Knowledge-based model construction Markov chain Monte Carlo Pseudo-likelihood Link prediction |
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