Intelligent Search of Full-Text Databases

Abstract

This project applies expert system technology to the task of searching online collections of documents. We are developing an intelligent search intermediary to help end-users locate relevant passages in large full- text databases. Our expert system will automatically reformulate contextual Boolean queries to improve search results and will present retrieved passages in decreasing order of relevance. It differs from other intelligent database functions in two ways: it works with semantically unprocessed text and the expert systems contains a knowledge base of search strategies independent of any particular content domain. The goals for our current project are to demonstrate the feasibility of the approach and to evaluate the effectiveness of the system through a controlled experiment. While the work we report here has limited objectives, the system and techniques are general and can be extended to large, real-world databases. Keywords: Computer architecture high level query languages, MICROARRAS system, C programming language. Author

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1987
Accession Number
ADA201088

Entities

People

  • John B. Smith
  • Susan Gauch

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Computer Programming
  • Computer Programs
  • Computer Science
  • Computers
  • Computing System Architectures
  • Database Management Systems
  • Databases
  • Efficiency
  • Expert Systems
  • Frequency
  • Information Retrieval
  • Information Systems
  • Language
  • Library Science
  • Natural Languages
  • User Interface

Fields of Study

  • Computer science
  • Engineering

Readers

  • Computational Linguistics