Please use this identifier to cite or link to this item: doi:10.22028/D291-24824
Title: A hybrid approach for modeling uncertainty in terminological logics
Author(s): Heinsohn, Jochen
Language: English
Year of Publication: 1991
OPUS Source: Kaiserslautern ; Saarbrücken : DFKI, 1991
SWD key words: Künstliche Intelligenz
Terminologische Sprache
DDC notations: 004 Computer science, internet
Publikation type: Report
Abstract: This paper proposes a probabilistic extension of terminological logics. The extension maintains the original performance of drawing inferences in a hierarchy of terminological definitions. It enlarges the range of applicability to real world domains determined not only by definitional but also by uncertain knowledge. First, we introduce the propositionally complete terminological language ALC. On the basis of the language construct "probabilistic implication" it is shown how statistical information on concept dependencies can be represented. To guarantee (terminological and probabilistic) consistency, several requirements have to be met. Moreover, these requirements allow one to infer implicitly existent probabilistic relationships and their quantitative computation. By explicitly introducing restrictions for the ranges derived by instantiating the consistency requirements, exceptions can also be handled. In the categorical cases this corresponds to the overriding of properties in non monotonic inheritance networks. Consequently, our model applies to domains where both term descriptions and non-categorical relations between term extensions have to be represented.
Link to this record: urn:nbn:de:bsz:291-scidok-35698
Series name: Research report / Deutsches Forschungszentrum für Künstliche Intelligenz [ISSN 0946-008x]
Series volume: 91-24
Date of registration: 16-May-2011
Faculty: SE - Sonstige Einrichtungen
Department: SE - DFKI Deutsches Forschungszentrum für Künstliche Intelligenz
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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