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
%0 Journal Article
%1 richardson2006markov
%A Richardson, Matthew
%A Domingos, Pedro
%D 2006
%I Kluwer Academic Publishers
%J Machine Learning
%K logic markov mln networs seminar2014
%N 1-2
%P 107-136
%R 10.1007/s10994-006-5833-1
%T Markov logic networks
%U http://dx.doi.org/10.1007/s10994-006-5833-1
%V 62
%X 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
@article{richardson2006markov,
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 },
added-at = {2014-10-20T11:37:40.000+0200},
author = {Richardson, Matthew and Domingos, Pedro},
biburl = {https://www.bibsonomy.org/bibtex/2fe0ea6ac59f2d4532e511c0b610545f3/jil},
description = {Markov logic networks - Springer},
doi = {10.1007/s10994-006-5833-1},
interhash = {dbe71b43896a6b798e837f1aa8fc391f},
intrahash = {fe0ea6ac59f2d4532e511c0b610545f3},
issn = {0885-6125},
journal = {Machine Learning},
keywords = {logic markov mln networs seminar2014},
language = {English},
number = {1-2},
pages = {107-136},
publisher = {Kluwer Academic Publishers},
timestamp = {2014-10-20T11:40:40.000+0200},
title = {Markov logic networks},
url = {http://dx.doi.org/10.1007/s10994-006-5833-1},
volume = 62,
year = 2006
}