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A central question for knowledge representation is how to encode and handleuncertain knowledge adequately. We introduce the probabilistic descriptionlogic ALCP that is designed for representing context-dependent knowledge, wherethe actual context taking place is uncertain. ALCP allows the expression oflogical dependencies on the domain and probabilistic dependencies on thepossible contexts. In order to draw probabilistic conclusions, we employ theprinciple of maximum entropy. We provide reasoning algorithms for this logic,and show that it satisfies several desirable properties of probabilisticlogics.

Probabilistic Reasoning in the Description Logic ALCP with the Principle of Maximum Entropy (Full Version)