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. 
Fabio Gagliardi Cozman, Rodrigo Bellizia Polastro
cs.AI