Practical Markov Logic Containing First-Order Quantifiers With Application To Identity Uncertainty

Markov logic is a highly expressive language recently introduced to specify the connectivity of a Markov network using first-order logic. While Markov logic is capable of constructing arbitrary first-order formulae over the data, the complexity of these formulae is often limited in practice because of the size and connectivity of the resulting network. In this paper, we present approximate inference and training methods that incrementally instantiate portions of the network as needed to enable first-order existential and universal quantifiers in Markov logic networks. When applied to the problem of object identification, this approach results in a conditional probabilistic model that can reason about objects, combining the expressivity of recently introduced BLOG models with the predictive power of conditional training. We validate our algorithms on the tasks of citation matching and author disambiguation.