Lifted Generative Parameter Learning

Statistical relational learning (SRL) augments probabilistic models with relational representations and facilitates reasoning over sets of objects. When learning the probabilistic parameters for SRL models, however, one often resorts to reasoning over individual objects. To address this challenge, we compile a Markov logic network into a compact and efficient first-order data structure and use weighted first-order model counting to exactly optimize the likelihood of the parameters in a lifted manner. By exploiting the relational structure in the model, it is possible to learn more accurate parameters and dramatically improve the run time of the likelihood calculation. This allows us to calculate the exact likelihood for models where previously only approximate inference was feasible. Results on real-world data sets show that this approach learns more accurate models.