Probabilistic Reasoning in the Description Logic ALCP with the Principle of Maximum Entropy (Full Version)
Artificial Intelligence
2016-07-01 v1 Logic in Computer Science
Abstract
A central question for knowledge representation is how to encode and handle uncertain knowledge adequately. We introduce the probabilistic description logic ALCP that is designed for representing context-dependent knowledge, where the actual context taking place is uncertain. ALCP allows the expression of logical dependencies on the domain and probabilistic dependencies on the possible contexts. In order to draw probabilistic conclusions, we employ the principle of maximum entropy. We provide reasoning algorithms for this logic, and show that it satisfies several desirable properties of probabilistic logics.
Keywords
Cite
@article{arxiv.1606.09521,
title = {Probabilistic Reasoning in the Description Logic ALCP with the Principle of Maximum Entropy (Full Version)},
author = {Rafael Peñaloza and Nico Potyka},
journal= {arXiv preprint arXiv:1606.09521},
year = {2016}
}
Comments
Full version of paper accepted at the Tenth International Conference on Scalable Uncertainty Management (SUM 2016)