Stratified Knowledge Bases as Interpretable Probabilistic Models
  (Extended Abstract)
  In this paper, we advocate the use of stratified logical theories for
representing probabilistic models. We argue that such encodings can be more
interpretable than those obtained in existing frameworks such as Markov logic
networks. Among others, this allows for the use of domain experts to improve
learned models by directly removing, adding, or modifying logical formulas.
