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Non-Standard Inferences in Description Logics [electronic resource] : From Foundations and Definitions to Algorithms and Analysis /

By: Contributor(s): Material type: TextTextSeries: Lecture Notes in Artificial Intelligence ; 2100Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2001Edition: 1st ed. 2001Description: X, 254 p. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783540446132
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 006.3 23
LOC classification:
  • Q334-342
  • TA347.A78
Online resources:
Contents:
Description Logics -- Non-Standard Inferences -- Characterizing Subsumption -- LCS and MSC -- Matching -- Conclusion.
In: Springer Nature eBookSummary: Description logics (DLs) are used to represent structured knowledge. Inference services testing consistency of knowledge bases and computing subconcept/superconcept hierarchies are the main feature of DL systems. Intensive research during the last fifteen years has led to highly optimized systems that allow to reason about knowledge bases efficiently. However, applications often require additional non-standard inferences to support both the construction and the maintenance of knowledge bases, thus making the inference procedures again incomplete. This book, which is a revised version of the author's PhD thesis, constitutes a significant step to fill this gap by providing an excellent formal foundation of the most prominent non-standard inferences. The descriptions given include precise definitions, complete algorithms and thorough complexity analysis. With its solid foundation, the book also serves as a basis for future research.
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Description Logics -- Non-Standard Inferences -- Characterizing Subsumption -- LCS and MSC -- Matching -- Conclusion.

Description logics (DLs) are used to represent structured knowledge. Inference services testing consistency of knowledge bases and computing subconcept/superconcept hierarchies are the main feature of DL systems. Intensive research during the last fifteen years has led to highly optimized systems that allow to reason about knowledge bases efficiently. However, applications often require additional non-standard inferences to support both the construction and the maintenance of knowledge bases, thus making the inference procedures again incomplete. This book, which is a revised version of the author's PhD thesis, constitutes a significant step to fill this gap by providing an excellent formal foundation of the most prominent non-standard inferences. The descriptions given include precise definitions, complete algorithms and thorough complexity analysis. With its solid foundation, the book also serves as a basis for future research.

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