Advertisement

Information Retrieval Techniques in Rule-based Expert Systems

  • Jiri Panyr
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

In rule-based expert systems knowledge is represented in an IF-THEN form: IF <set of conditions> THEN <decision>. A limited subset of natural language — supplemented by specified relations and operators — is used to formulate the rules. Rule syntax is simple. This makes it easy to acquire knowledge through an expert and permits plausibility checks on the knowledge base without the expert having knowledge of the implementation language or details of the system. A number of steps are used to select suitable rules during the rule-matching process. It is noteworthy that rules are well structured documents for an information retrieval system, particularly since the number of rules in a rule-based system remains manageable. In this paper it will be shown that this permits automatic processing of the rule set by methods of information retrieval (i.e. automatic indexing and automatic classification of rules, automatic thesaurus construction to the knowledge base) . A knowledge base which is processed and structured in this fashion allows the use of a complex application-specific search strategy and hence an efficient and effective realization of reasoning mechanisms.

Keywords

Information Retrieval Expert System Automatic Classification Information Retrieval System Rule Interpreter 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Bibliography

  1. Forgy, C.L. (1982): Rete: A Fast Algorithm for the Many Pattern/Many Object Pattern Match Problem. In: Artificial Intelligence 19 17–37.CrossRefGoogle Scholar
  2. Jackson, P. (1986): Introduction to Expert Systems. Addison-Wesley Publ. Comp., Wokingham (England), Reading (Mass.) et al.Google Scholar
  3. Kuhlen, R. (1985): Verarbeitung von Daten, Repräsentation von Wissen, Erarbeitung von Informationen. Primat der Pragmatik bei informationeller Sprachverarbeitung. Bericht 7/85. Univ. Konstanz (Informationswissenschaft), Konstanz.Google Scholar
  4. Michalski, R.S.; Stepp, R.E. (1983): Learning from Observation: Conceptual Clustering. In: R.S. Michalski, J.G. Carbonell, T.M. Mitchell (ed.): Machine Learning — An Artificial Intelligence Approach. Tioga Publ.Comp., Palo Alto (Calif.).Google Scholar
  5. Panyr; J. (1986a): Automatische Klassifikation und Information Retrieval. Max Niemeyer Verlag, Tübingen.Google Scholar
  6. Panyr, J. (1986b): Die Theorie der Fuzzy-Mengen und Information-Retrieval-Systeme. Nachr. für Dokum. 37 163–168.Google Scholar
  7. Panyr, J. (1987): Vektorraum-Modell und Clusteranalyse in Information-Retrie-val-Systemen. Nachr. für. Dokum. 38 13–20.Google Scholar
  8. Panyr, J.; Schütt, D. (1988): Wissensgestützte Fertigungsplanung. Siemens Forsch.-u. Entwickl.-Ber. 17 95–98.Google Scholar
  9. Rieger, B. (1984): Semantische Dispositionen — prozedurale Wissensstrukturen mit stereotypisch repräsentierten Wortbedeutungen. In: B. Rieger (ed.): Dynamik der Bedeutungskonstitution.Google Scholar
  10. Williams, T.; Bainbridge, B. (1988): Rule Based Systems. In: G.A. Ringland, D.A. Duce (ed.): Approaches to Knowledge Representation — An Introduction. John Wiley & sons Inc., New York.Google Scholar
  11. Winston, P.H. (1987): Künstliche Intelligenz. Addison-Wesley Publ. Comp., Bonn, Reading (Mass.) et al.Google Scholar

Copyright information

© Springer-Verlag Berlin · Heidelberg 1991

Authors and Affiliations

  • Jiri Panyr
    • 1
  1. 1.Siemens AG, ZFE IS SYS 5MünchenGermany

Personalised recommendations