Acquisition of Terminological Knowledge in Probabilistic Description Logic

  • Francesco KriegelEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11117)


For a probabilistic extension of the description logic \({\mathcal {\mathcal {E\!L}}}^{\!\bot }\), we consider the task of automatic acquisition of terminological knowledge from a given probabilistic interpretation. Basically, such a probabilistic interpretation is a family of directed graphs the vertices and edges of which are labeled, and where a discrete probability measure on this graph family is present. The goal is to derive so-called concept inclusions which are expressible in the considered probabilistic description logic and which hold true in the given probabilistic interpretation. A procedure for an appropriate axiomatization of such graph families is proposed and its soundness and completeness is justified.


Data mining Knowledge acquisition Probabilistic description logic Knowledge base Probabilistic interpretation Concept inclusion 



The author gratefully thanks Franz Baader for drawing attention to the issue in [6], and furthermore thanks the anonymous reviewers for their constructive hints and helpful remarks.


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Institute of Theoretical Computer ScienceTechnische Universität DresdenDresdenGermany

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