An approach to natural language processing in the rule-based expert system

ABSTRACT

This paper deals with the transformation of knowledge expressed in the natural language into symbolic expressions. It is shown that rules supplied from experts can be transformed by computer into such symbolic expressions as enable using the logic programming to make the plausible inferences. These symbolic expressions are stored in the knowledge base and represent the knowledge acquired from experts. First, the synthesis of the symbolic expression representing the initial knowledge in the knowledge base is considered. Then, the problem of acquiring new elements of knowledge for the knowledge base is argued. Finally, a way of transforming the symbolic expressions into PROLOG rules is introduced.

REFERENCES

• 1: Michael R. Genesereth , Matthew L. Ginsberg, Logic programming, Communications of the ACM, v.28 n.9, p.933-941, Sept. 1985 [doi>10.1145/4284.4287]
• 2: Frederick Hayes-Roth, Rule-based systems, Communications of the ACM, v.28 n.9, p.921-932, Sept. 1985 [doi>10.1145/4284.4286]
• 3: Kazimierczak J., Knowledge Acquisition from User Programs by Computer with Own Knowledge -Based System, in: "Expert Systems and Artificial Intelligence in Decision Support System", pp. 271-292, D.Reidel Publishing Company, Dordrecht, The Netherlands, 1987.
• 4: Kazimierczak J., Acquisition of Knowledge and Its Integration in Knowledge Base of the Rule-Based Expert System, "Papers on Science of Science and Forecasting", No. 48-49, pp. 23-38, Wroclaw Technical University Press, Wroclaw 1987.
• 5: S. Srinivasan , Pradip Dey , Yoichi Hayashi, A flexible interactive control structure for rule-based systems, Proceedings of the 1988 ACM sixteenth annual conference on Computer science, p.447-453, February 1988, Atlanta, Georgia, United States [doi>10.1145/322609.322791]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.1 Applications and Expert Systems

Additional Classification:
  G. Mathematics of Computing
  G.2 DISCRETE MATHEMATICS
      G.2.2 Graph Theory
          Subjects: Graph algorithms

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.3 Deduction and Theorem Proving
          Subjects: Logic programming
      I.2.4 Knowledge Representation Formalisms and Methods
          Subjects: Representations (procedural and rule-based)
      I.2.6 Learning
          Subjects: Knowledge acquisition
      I.2.7 Natural Language Processing

General Terms:
Algorithms, Languages, Theory