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.
CITATION STYLE
Peñaloza, R., & Potyka, N. (2016). Probabilistic reasoning in the description logic ALCP with the principle of maximum entropy. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9858 LNAI, pp. 246–259). Springer Verlag. https://doi.org/10.1007/978-3-319-45856-4_17
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