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.
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Notes
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The full proofs are available at the technical report [23].
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Peñaloza, R., Potyka, N. (2016). Probabilistic Reasoning in the Description Logic \(\mathcal {ALCP}\) with the Principle of Maximum Entropy . In: Schockaert, S., Senellart, P. (eds) Scalable Uncertainty Management. SUM 2016. Lecture Notes in Computer Science(), vol 9858. Springer, Cham. https://doi.org/10.1007/978-3-319-45856-4_17
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