Since logical connections between relations are modeled explicitly, such approaches are generally hard to scale.
approaches based on Markov Logic Networks (MLNs) (Richardson and Domingos, 2006) encode logical knowledge in dense, loopy graphical models, making structure learning, parameter estimation, and inference hard for the scale of our data.