© 1989

Foundations of Knowledge Base Management

Contributions from Logic, Databases, and Artificial Intelligence Applications

  • Joachim W. Schmidt
  • Constantino Thanos

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

Table of contents

  1. Front Matter
    Pages I-XIV
  2. Logic and Knowledge Representation

    1. Front Matter
      Pages 1-1
    2. Wolfgang Bibel, Jean-Marie Nicolas
      Pages 3-22
    3. Robert Kowalski, Marek Sergot
      Pages 23-55
    4. Enrico Motta, Maria Simi, Giuseppe Attardi
      Pages 57-72
    5. Robert Demolombe, Luis Fariñas del Cerro
      Pages 73-89
    6. Amilcar Sernadas, Cristina Sernadas
      Pages 91-116
  3. From Data to Facts and Rules

    1. Front Matter
      Pages 117-117
    2. Hervé Gallaire, Jean-Marie Nicolas
      Pages 119-130
    3. Joachim W. Schmidt, Lingyuan Ge, Volker Linnemann, Matthias Jarke
      Pages 153-178
  4. Architectural Issues in Data and Knowledge Base Integration

    1. Front Matter
      Pages 203-203
    2. Michael L. Brodie, Frank Manola
      Pages 205-240
    3. Erich J. Neuhold, Michael Schrefl
      Pages 241-257
    4. John Miles Smith
      Pages 259-281
    5. Antonio Albano, Giuseppe Attardi
      Pages 283-291
    6. Brian A. Nixon, K. Lawrence Chung, David Lauzon, Alex Borgida, John Mylopoulos, Martin Stanley
      Pages 293-343
    7. Janis A. Bubenko, Istvan P. Orci
      Pages 373-378

About this book


In the past, applied artificial intelligence systems were built with particular emphasis on general reasoning methods intended to function efficiently, even when only relatively little domain-specific knowledge was available. In other words, AI technology aimed at the processing of knowledge stored under comparatively general representation schemes. Nowadays, the focus has been redirected to the role played by specific and detailed knowledge, rather than to the reasoning methods themselves. Many new application systems are centered around knowledge bases, i. e. , they are based on large collections offacts, rules, and heuristics that cap­ ture knowledge about a specific domain of applications. Experience has shown that when used in combination with rich knowledge bases, even simple reasoning methods can be extremely effective in a wide variety of problem domains. Knowledge base construction and management will thus become the key factor in the development of viable knowledge-based ap­ plications. Knowledge Base Management Systems (KBMSs) are being proposed that provide user-friendly environments for the construction, retrieval, and manipUlation of large shared knowledge bases. In addition to deductive reasoning, KBMSs require operational characteristics such as concurrent access, integrity maintenance, error recovery, security, and perhaps distribution. For the development ofKBMSs, the need to integrate concepts and technologies from different areas, such as Artificial Intel­ ligence, Databases, and Logic, has been widely recognized. One of the central issues for KBMSs is the framework used for knowledge representation-semantic networks, frames, rules, and logics are proposed by the AI and logic communities.


Extension artificial intelligence data model database databases deductive database information system intelligence knowledge knowledge base knowledge representation knowledge-based system logic logic programming programming

Editors and affiliations

  • Joachim W. Schmidt
    • 1
  • Constantino Thanos
    • 2
  1. 1.Fachbereich InformatikJohann Wolfgang Goethe-UniversitätFrankfurt a. M. 11Rep. Fed. of Germany
  2. 2.Istituto di Elaborazione della InformazioneConsiglia Nazionale delle Ricerche (CNR)PisaItaly

Bibliographic information

Industry Sectors
Chemical Manufacturing
IT & Software
Consumer Packaged Goods
Materials & Steel
Finance, Business & Banking
Energy, Utilities & Environment