Markov Logic Networks
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.
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