Axiom-based Probabilistic Description Logic

Martin Unold, Christophe Cruz

Abstract

The paper proposes a new type of probabilistic description logics (p-DLs) with a different interpretation of uncertain knowledge. In both approaches (classical state of the art approaches and the approach of this paper), probability values are assigned to axioms in a knowledge base. While In classical p-DLs, the probability value of an axiom is interpreted as the probability of the axiom to be true in contrast to be false or unknown, the probability value in this approach is interpreted as the probability of an the axiom to be true in contrast to other axioms being true. The paper presents the theory of that novel approach and a method for the treatment of such data. The proposed description logic is evaluated with some sample knowledge bases and the results are discussed.

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