Complexity Analysis and Variational Inference for Interpretation-Based Probabilistic Description Logic
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
Text
Cozman and Polastro. "Complexity Analysis and Variational Inference for Interpretation-Based Probabilistic Description Logic." Conference on Uncertainty in Artificial Intelligence, 2009.Markdown
[Cozman and Polastro. "Complexity Analysis and Variational Inference for Interpretation-Based Probabilistic Description Logic." Conference on Uncertainty in Artificial Intelligence, 2009.](https://mlanthology.org/uai/2009/cozman2009uai-complexity/)BibTeX
@inproceedings{cozman2009uai-complexity,
title = {{Complexity Analysis and Variational Inference for Interpretation-Based Probabilistic Description Logic}},
author = {Cozman, Fábio Gagliardi and Polastro, Rodrigo Bellizia},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2009},
pages = {117-125},
url = {https://mlanthology.org/uai/2009/cozman2009uai-complexity/}
}