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On Knowledge Base Management Systems

Integrating Artificial Intelligence and Database Technologies

  • Book
  • © 1986

Overview

Editors:
  1. Micheal L. Brodie

    1. Computer Corporation of America, Cambridge, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. John Mylopoulos

    1. Department of Computer Science, University of Toronto, Toronto, Canada

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Topics in Information Systems (TINF)

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Table of contents (47 chapters)

  1. Front Matter

    Pages i-xxi
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  2. Knowledge Base Management Systems

    1. Front Matter

      Pages 1-1
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    2. On Knowledge Base Management Systems

      • John Mylopoulos
      Pages 3-8

    3. The Knowledge Level of a KMBS

      • Ron Brachman, Hector Levesque
      Pages 9-12

    4. Knowledge Level Interfaces to Information Systems

      • Hector J. Levesque, Ronald J. Brachman
      Pages 13-34

    5. On Knowledge-Based System Architectures

      • Frank Manola, Michael L. Brodie
      Pages 35-54

    6. Discussion

      • Micheal L. Brodie, John Mylopoulos
      Pages 55-59

  3. Knowledge Bases versus Databases

    1. Front Matter

      Pages 61-61
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    2. A View Of Knowledge Representation

      • Hector J. Levesque
      Pages 63-69

    3. AI Knowledge Bases And Databases

      • David Israel
      Pages 71-75

    4. Knowledge versus Data

      • Gio Wiederhold
      Pages 77-82

    5. Knowledge Bases vs Databases

      • Michael L. Brodie, John Mylopoulos
      Pages 83-86

    6. Knowledge Based and Database Systems: Enhancements, Coupling or Integration?

      • Yannis Vassiliou
      Pages 87-91

    7. Knowledge Based and Database Systems: Enhancements, Coupling or Integration?

      • Micheal L. Brodie, John Mylopoulos
      Pages 93-94

  4. Retrieval/Interface/Reasoning

    1. Front Matter

      Pages 95-95
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    2. Notes on Inference: A Somewhat Skewed Survey

      • David Israel
      Pages 97-109

    3. Current Trends in Database Query Processing

      • Matthias Jarke
      Pages 111-119

    4. Logic and Database Systems

      • Jeffrey D. Ullman
      Pages 121-123

    5. Negation in Knowledge Base Management Systems

      • Shamin A. Naqvi
      Pages 125-145

    6. An Approach To Processing Queries In A Logic-Based Query Language

      • Jeffrey D. Ullman
      Pages 147-164
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Keywords

About this book

Current experimental systems in industry, government, and the military take advantage of knowledge-based processing. For example, the Defense Advanced Research Projects Agency (DARPA), and the United States Geological Survey (USGS) are supporting the develop­ ment of information systems that contain diverse, vast, and growing repositories of data (e.g., vast databases storing geographic informa­ tion). These systems require powerful reasoning capabilities and pro­ cessing such as data processing, communications, and multidisciplinary of such systems will scientific analysis. The number and importance grow significantly in the near future. Many of these systems are severely limited by current knowledge base and database systems technology. Currently, knowledge-based system technology lacks the means to provide efficient and robust knowledge bases, while database system technology lacks knowledge representation and reasoning capabilities. The time has come to face the complex research problems that must be solved before we can design and implement real, large scale software systems that depend on knowledge-based processing. To date there has been little research directed at integrating knowledge base and database technologies. It is now imperative that such coordinated research be initiated and that it respond to the urgent need for a tech­ nology that will enable operational large-scale knowledge-based system applications.

Editors and Affiliations

  • Computer Corporation of America, Cambridge, USA

    Micheal L. Brodie

  • Department of Computer Science, University of Toronto, Toronto, Canada

    John Mylopoulos

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