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11

ML
2006
ACM

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Markov logic networks

13 years 4 months ago

Markov logic networks

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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.

Matthew Richardson, Pedro Domingos

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First-order Knowledge Base | Machine Learning | Markov Logic Network | ML 2006 | Probabilistic Graphical Models |

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Added	14 Dec 2010
Updated	14 Dec 2010
Type	Journal
Year	2006
Where	ML
Authors	Matthew Richardson, Pedro Domingos

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