Markov logic networks Export

@article{citeulike:501539, 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.}, author = {Richardson, Matthew and Domingos, Pedro}, citeulike-article-id = {501539}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1113910}, citeulike-linkout-1 = {http://dx.doi.org/10.1007/s10994-006-5833-1}, citeulike-linkout-2 = {http://www.ingentaconnect.com/content/klu/ml/2006/00000062/F0020001/00005833}, doi = {10.1007/s10994-006-5833-1}, issn = {0885-6125}, journal = {Machine Learning}, keywords = {graphicalmodel, mln, mlns}, month = {February}, number = {1-2}, pages = {107--136}, posted-at = {2009-11-03 05:10:18}, priority = {2}, publisher = {Springer}, title = {Markov logic networks}, url = {http://dx.doi.org/10.1007/s10994-006-5833-1}, volume = {62}, year = {2006} }

TY - JOUR ID - citeulike:501539 L3 - citeulike-article-id:501539 N2 - 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. IS - 1-2 JF - Machine Learning SN - 0885-6125 EP - 136 TI - Markov logic networks PB - Springer VL - 62 SP - 107 KW - graphicalmodel KW - mln KW - mlns AU - Richardson, Matthew AU - Domingos, Pedro PY - 2006/02// UR - http://dx.doi.org/10.1007/s10994-006-5833-1 ER -