@article{432,
  keywords = {Knowledge representation and reasoning, databases, semantic web reasoning},
  author = {Leonard Botha and Tommie Meyer and Rafael Penaloza},
  title = {The Probabilistic Description Logic BALC},
  abstract = {Description logics (DLs) are well-known knowledge representation formalisms focused on the representation of terminological knowledge. Due to their first-order semantics, these languages (in their classical form) are not suitable for representing and handling uncertainty. A probabilistic extension of a light-weight DL was recently proposed for dealing with certain knowledge occurring in uncertain contexts. In this paper, we continue that line of research by introducing the Bayesian extension BALC of the propositionally closed DL ALC. We present a tableau-based procedure for deciding consistency and adapt it to solve other probabilistic, contextual, and general inferences in this logic. We also show that all these problems remain ExpTime-complete,
the same as reasoning in the underlying classical ALC.},
  year = {2020},
  journal = {Theory and Practice of Logic Programming},
  chapter = {1-24},
  publisher = {Cambridge University Press},
  doi = {10.1017/S1471068420000460},
}