An Expert System for Searching in Full-Text

Abstract

This dissertation explores techniques to improve full text information retrieval by experienced computer users who are novice users of retrieval systems. An expert system which automatically reformulates Boolean user queries to improve search results is presented. The expert system differs from other intelligent database functions in two ways: it works with semantically and syntactically unprocessed text; and the expert system contains a knowledge base of domain independent search strategies. The passages retrieved are presented to the user in decreasing order of estimated relevancy. This combination of user interface features provides powerful, yet simple, access to full-text documents. Experimental results demonstrate that the expert system can improve the search efficiency of novice searchers without decreasing their search effectiveness. Further, an evaluation of the ranking algorithm confirms that, in general, the system presents potentially relevant passages to the user before irrelevant passages.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1989
Accession Number
ADA236682

Entities

People

  • Susan Gauch

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Biomedical
  • C4I

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Central Processing Units
  • Computer Architecture
  • Computer Programming
  • Computer Science
  • Computers
  • Expert Systems
  • Floating Point Operations
  • Grammars
  • Information Processing
  • Information Retrieval
  • Information Systems
  • Language
  • Natural Language Processing
  • Natural Languages
  • Operating Systems
  • Word Processors

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Geospatial Intelligence and Artificial Intelligence Analytics
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Information Retrieval