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Report (Bericht) zugänglich unter
URN: urn:nbn:de:bsz:291-scidok-35698
URL: http://scidok.sulb.uni-saarland.de/volltexte/2011/3569/


A hybrid approach for modeling uncertainty in terminological logics

Heinsohn, Jochen

Quelle: (1991) Kaiserslautern ; Saarbrücken : DFKI, 1991
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Dokument 1.pdf (11.969 KB)

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SWD-Schlagwörter: Künstliche Intelligenz , Terminologische Sprache
Institut: DFKI Deutsches Forschungszentrum für Künstliche Intelligenz
DDC-Sachgruppe: Informatik
Dokumentart: Report (Bericht)
Schriftenreihe: Research report / Deutsches Forschungszentrum für Künstliche Intelligenz [ISSN 0946-008x]
Bandnummer: 91-24
Sprache: Englisch
Erstellungsjahr: 1991
Publikationsdatum: 16.05.2011
Kurzfassung auf Englisch: 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.
Lizenz: Standard-Veröffentlichungsvertrag

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