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Most subfields of computer science have an interface layer via
which applications communicate with the infrastructure, and this is
key to their success (e.g., the Internet in networking, the
relational model in databases, etc.). So far this interface layer
has been missing in AI. First-order logic and probabilistic
graphical models each have some of the necessary features, but a
viable interface layer requires combining both. Markov logic is a
powerful new language that accomplishes this by attaching weights
to first-order formulas and treating them as templates for features
of Markov random fields. Most statistical models in wide use are
special cases of Markov logic, and first-order logic is its
infinite-weight limit. Inference algorithms for Markov logic
combine ideas from satisfiability, Markov chain Monte Carlo, belief
propagation, and resolution. Learning algorithms make use of
conditional likelihood, convex optimization, and inductive logic
programming. Markov logic has been successfully applied to problems
in information extraction and integration, natural language
processing, robot mapping, social networks, computational biology,
and others, and is the basis of the open-source Alchemy system.
Table of Contents: Introduction / Markov Logic / Inference /
Learning / Extensions / Applications / Conclusion
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