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by Ralf Küsters
Download Non-Standard Inferences in Description Logics: From Foundations and Definitions to Algorithms and Analysis (Lecture Notes in Computer Science) fb2
Computer Science
  • Author:
    Ralf Küsters
  • ISBN:
    3540423974
  • ISBN13:
    978-3540423973
  • Genre:
  • Publisher:
    Springer; 2001 edition (September 6, 2001)
  • Pages:
    254 pages
  • Subcategory:
    Computer Science
  • Language:
  • FB2 format
    1254 kb
  • ePUB format
    1858 kb
  • DJVU format
    1785 kb
  • Rating:
    4.9
  • Votes:
    221
  • Formats:
    mbr docx doc lrf


Authors: Küsters, Ralf. Non-Standard Inferences.

Authors: Küsters, Ralf. Description logics (DLs) are used to represent structured knowledge. Inference services testing consistency of knowledge bases and computing ept hierarchies are the main feature of DL systems. The descriptions given include precise definitions, complete algorithms and thorough complexity analysis.

Description logics (DLs) are used to represent structured knowledge. Inference services testing consistency of knowledge bases and computing ept hierarchies are the main feature of.

Descriptions given include precise definitions, complete algorithms and thorough complexity analysis. Download (pdf, . 0 Mb) Donate Read. Epub FB2 mobi txt RTF. Converted file can differ from the original. If possible, download the file in its original format.

Non-Standard Inferences . .has been added to your Basket. 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.has been added to your Cart. Flip to back Flip to front. Non-Standard Inferences in Description Logics: From Foundations and Definitions to Algorithms and Analysis Paperback – 25 July 2001. by Ralf Kusters (Author).

oceedings{dII, title {Non-Standard Inferences in Description Logics}, author {Ralf K{"u}sters} . Characterizing Subsumption. LCS and MS. Matching.

oceedings{dII, title {Non-Standard Inferences in Description Logics}, author {Ralf K{"u}sters}, booktitle {Lecture Notes in Computer Science}, year {2001} .

Conference Paper in Lecture Notes in Computer Science 2850:122-136 .

Conference Paper in Lecture Notes in Computer Science 2850:122-136 · September 2003 with 3 Reads. How we measure 'reads'. Description Logics (DLs) are a family of knowledge representation formalisms used for terminological reasoning. Non-standard inferences are a group of relatively new inference services which provide reasoning support for the building, maintaining, and deployment of DL knowledge-bases.

Non-Standard Inferences in Description Logics : From Foundations and Definitions to Algorithms and Analysis. by Ralf Kusters and Ralf Küsters.

Lecture Notes in Computer Science is a series of computer science books published by Springer Science+Business Media since 1973. The series contains proceedings, post-proceedings, and monographs. In addition, tutorials, state-of-the-art surveys, and "hot topics" are increasingly being included. Two sub-series are: Lecture Notes in Artificial Intelligence. Lecture Notes in Bioinformatics. Monographiae Biologicae, another monograph series published by Springer Science+Business Media.

Lecture Notes in Computer Science . However, algorithms based on -cuttings seem to provide a challenge for implementation. In other ap-plications, the productivity of the user of a software tool is closely positively related to the tool’s performance.

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.