Complexity Analysis and Variational Inference for Interpretation-based
  Probabilistic Description Logic
  This paper presents complexity analysis and variational methods for inference
in probabilistic description logics featuring Boolean operators,
quantification, qualified number restrictions, nominals, inverse roles and role
hierarchies. Inference is shown to be PEXP-complete, and variational methods
are designed so as to exploit logical inference whenever possible.
