Complexity Analysis and Variational Inference for Interpretation-based Probabilistic Description Logic
Artificial Intelligence
2012-05-14 v1
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.
Cite
@article{arxiv.1205.2652,
title = {Complexity Analysis and Variational Inference for Interpretation-based Probabilistic Description Logic},
author = {Fabio Gagliardi Cozman and Rodrigo Bellizia Polastro},
journal= {arXiv preprint arXiv:1205.2652},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI2009)