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Probabilistic Reasoning in the Description Logic \(\mathcal {ALCP}\) with the Principle of Maximum Entropy

  • Rafael PeñalozaEmail author
  • Nico Potyka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9858)

Abstract

A central question for knowledge representation is how to encode and handle uncertain knowledge adequately. We introduce the probabilistic description logic \(\mathcal {ALCP}\) that is designed for representing context-dependent knowledge, where the actual context taking place is uncertain. \(\mathcal {ALCP}\) 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

Maximum Entropy Description Logic Probabilistic Logic Probabilistic Constraint Propositional Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Free University of Bozen-BolzanoBolzanoItaly
  2. 2.University of OsnabrückOsnabrückGermany

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