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Titel: A hybrid approach for modeling uncertainty in terminological logics
Verfasser: Heinsohn, Jochen
Sprache: Englisch
Erscheinungsjahr: 1991
Quelle: Kaiserslautern ; Saarbrücken : DFKI, 1991
SWD-Schlagwörter: Künstliche Intelligenz
Terminologische Sprache
DDC-Sachgruppe: 004 Informatik
Dokumentart : Report (Bericht)
Kurzfassung: 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 zu diesem Datensatz: urn:nbn:de:bsz:291-scidok-35698
hdl:20.500.11880/24880
http://dx.doi.org/10.22028/D291-24824
Schriftenreihe: Research report / Deutsches Forschungszentrum für Künstliche Intelligenz [ISSN 0946-008x]
Band: 91-24
SciDok-Publikation: 16-Mai-2011
Fakultät: Sonstige Einrichtungen
Fachrichtung: SE - DFKI Deutsches Forschungszentrum für Künstliche Intelligenz
Fakultät / Institution:SE - Sonstige Einrichtungen

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