Lifted Weight Learning of Markov Logic Networks Revisited

In this paper, we complete the work of [14] by answering We study lifted weight learning of Markov whether maximum-likelihood learning of MLNs logic networks. We show that there is an algorithm can be done in time polynomial in the size of the domain for maximum-likelihood learning of for 2-variable MLNs. We give a positive answer 2-variable Markov logic networks which runs to this question (Theorem 11), under consideration of in time polynomial in the domain size. Our the dependence of the runtime bounds on how extreme results are based on existing lifted-inference the statistics of the training data are. To arrive at this algorithms and recent algorithmic results on positive result, we need to combine results from three computing maximum entropy distributions.