Axiom pinpointing has been introduced in description logics (DLs) to help the user to understand the reasons why consequences hold and to remove unwanted consequences by computing minimal (maximal) subsets of the knowledge base that have (do not have) the consequence in question. The pinpointing algorithms described in the DL literature are obtained as extensions of the standard tableau-based reasoning algorithms for computing consequences from DL knowledge bases. Although these extensions are based on similar ideas, they are all introduced for a particular tableau-based algorithm for a particular DL.

The purpose of this paper is to develop a general approach for extending a tableau-based algorithm to a pinpointing algorithm. This approach is based on a general definition of “tableaux algorithms,” which captures many of the known tableau-based algorithms employed in DLs, but also other kinds of reasoning procedures.


Description Logic Horn Clause Rule Application Valuation Versus Fresh Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Franz Baader
    • 1
  • Rafael Peñaloza
    • 2
  1. 1.Theoretical Computer Science, TU DresdenGermany
  2. 2.Intelligent Systems, University of LeipzigGermany

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