## Markov Logic

Pedro Domingos
,
Stanley Kok
,
Daniel Lowd
,
Hoifung Poon
,
Matthew Richardson
and
Parag Singla
### Abstract:

Most real-world machine learning problems have both statistical
and relational aspects. Thus learners need representations that
combine probability and relational logic. Markov logic accomplishes this
by attaching weights to first-order formulas and viewing them as templates
for features of Markov networks. Inference algorithms for Markov
logic draw on ideas from satisfiability, Markov chain Monte Carlo and
knowledge-based model construction. Learning algorithms are based on
the conjugate gradient algorithm, pseudo-likelihood and inductive logic
programming. Markov logic has been successfully applied to problems in
entity resolution, link prediction, information extraction and others, and
is the basis of the open-source Alchemy system.
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