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Knowledge based control of the LILOG inference engine: Kinds of metaknowledge

  • K. H. Bläsius
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 546)

Abstract

Investigations in the context of the LILOG project have shown that a knowledge based control of the inference engine may lead to significant improvements in efficiency. The object is to allow the specification of knowledge (metaknowledge) about the intended use of the real knowledge (object knowledge). For that purpose a special control language has been designed allowing the specification of metaknowledge, which is used by the inference engine for an immediate control of its inference steps. By that, a partial control of the knowledge processing is possible. The basis for the development of the actual control language was an analysis of different kinds of metaknowledge which have turned out to be important within the LILOG context. These different kinds of metaknowledge also demand different procedures for interpretation. In this paper the different kinds of metaknowledge are presented by means of several examples, from which we derive certain elements of the control language for the inference engine.

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References

  1. Bläsius K.H., Rollinger C.-R., Siekmann J.-H. (1990): Structure and control of the L-LILOG inference system. In: Bläsius K.H., Hedtstück U., Rollinger C.-R. (eds): Sorts and Types in Artificial Intelligence. Lecture Notes in Artificial Intelligence, Vol. 418, Springer-Verlag, Berlin, Heidelberg, pp. 165–182Google Scholar
  2. Bollinger T., Hedtstück U., Rollinger C.-R. (1989): Reasoning for text understanding — Knowledge processing in the 1st LILOG prototype. In: Metzing (ed): GWAI-89 13th German Workshop on Artificial Intelligence, Informatik Fachberichte 216, Springer-Verlag, Berlin, Heidelberg, pp. 203–212Google Scholar
  3. Eberle K. (1989): Quantifikation, Plural, Ereignisse und ihre Argumente in einer mehrsortigen Sprache der Prädikatenlogik erster Stufe. IWBS Report 67, IBM Deutschland, StuttgartGoogle Scholar
  4. Emde W., Schmiedel A. (1983): Aspekte der Verarbeitung unsicheren Wissens. KIT-Report 6, TU Berlin, FB Informatik, BerlinGoogle Scholar
  5. Klabunde K. (1989): Erweiterung der Wissensrepräsentationssprache L-LILOG um Konstrukte zur Spezifikation von Kontrollinformation. IWBS Report 92, IBM Deutschland, StuttgartGoogle Scholar
  6. Kowalski R. (1975): A proof procedure using connection graphs. Journal of ACM, 22 (4)Google Scholar
  7. Müller M. (1990): Implementierung und Integration von Verfahren zur wissensbasierten Steuerung der LELOG-Inferenzmaschine. Diplomarbeit, Fachhochschule DortmundGoogle Scholar
  8. Pletat U., v. Luck K. (1990): Knowledge representation in LILOG. In: Bläsius K.H., Hedtstück U., Rollinger C.-R. (eds): Sorts and Types in Artificial Intelligence. Lecture Notes in Artificial Intelligence, Vol. 418, Springer-Verlag, Berlin, Heidelberg, pp. 140–164Google Scholar
  9. Robinson J.A. (1965): A machine oriented logic based on the resolution principle. Journal of ACM, 12(1): pp. 23–41Google Scholar
  10. Röhrig R. (1991): Kommentar zu Verarbeitung von L-LILOG — Theorie und Praxis. In: Klose G., Lang E., Pirlein T. (eds.): Die Ontologie und Axiomatik der Wissensbank von LEU/2. IWBS Report 171, IBM Deutschland, Stuttgart, pp. IX–11–IX–14Google Scholar
  11. Schmiedel A. (1984): Eine Inferenzmaschine zur Verarbeitung unsicheren Wissens. In: Rollinger C. (eds.): Probleme des (Text-) Verstehens. Niemeyer-Verlag, TübingenGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 1991

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  • K. H. Bläsius

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