Access to data base systems via natural language

  • Klaus-Dieter Krägeloh
  • Peter C. Lockemann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 63)


Communication with computer via natural language is one of the major concerns of artificial intelligence. Modern approaches try to achieve this goal by simulating human language perception. The resulting models are highly complex, because the semantics of natural language statements must remain largely unrestricted. The communication with a commercially available data base system, on the other hand, deals with a heavily restricted formal model of the subject matter in the form of a data base. This is mainly due to the large amount of data that must be inspected and manipulated within reasonably short time. The semantics of any dialogue with the machine are such that all statements can be related to the formal model.

In light of this difference the definition of a natural query language for data base systems and the mapping of natural language statements to a modelling system will have to be approached in a manner different from artificial intelligence. In general one will try to make use of the results of linguistic research in artificial intelligence to the extent that they can take the simpler requirements of data base systems into account. In other cases different and perhaps more pragmatic solutions will be required.

The intent of this paper is to illustrate such an approach to natural language access to a data base system. In doing so a number of premises for natural language analysis in data base systems are developed concerning the syntactic model, morphemic analysis, semantic validity tests etc. These premises underly the work on a data base system that provides for a set-theoretic modelling system and a German language interface.


Natural Language Natural Language Processing Complex Category Word Stem Lexical Analysis 
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 1978

Authors and Affiliations

  • Klaus-Dieter Krägeloh
    • 1
  • Peter C. Lockemann
    • 1
  1. 1.Fakultät für InformatikUniversität KarlsruheDeutschland

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