AIMay 9, 2012
Complexity Analysis and Variational Inference for Interpretation-based Probabilistic Description Logic
arXiv:1205.2652v133 citations
Originality Incremental advance
AI Analysis
This work addresses inference challenges in probabilistic description logics, which is incremental as it builds on existing methods for a specific domain.
The paper tackled the problem of inference in probabilistic description logics with complex logical features, showing that inference is PEXP-complete and designing variational methods to leverage logical inference.
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.