Complexity analysis and variational inference for interpretation-based probabilistic description logics

  • Authors:
  • Fabio Gagliardi Cozman;Rodrigo Bellizia Polastro

  • Affiliations:
  • Universidade de São Paulo - São Paulo, SP - Brazil;Universidade de São Paulo - São Paulo, SP - Brazil

  • Venue:
  • UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
  • Year:
  • 2009

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Abstract

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.