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Foundations of Knowledge Base Management

Contributions from Logic, Databases, and Artificial Intelligence Applications

  • Book
  • © 1989

Overview

Editors:
  1. Joachim W. Schmidt

    1. Fachbereich Informatik, Johann Wolfgang Goethe-Universität, Frankfurt a. M. 11, Rep. Fed. of Germany

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  2. Constantino Thanos

    1. Istituto di Elaborazione della Informazione, Consiglia Nazionale delle Ricerche (CNR), Pisa, Italy

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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 (25 chapters)

  1. Front Matter

    Pages I-XIV
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  2. Logic and Knowledge Representation

    1. Front Matter

      Pages 1-1
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    2. The Role of Logic for Data and Knowledge Bases: A Brief Survey

      • Wolfgang Bibel, Jean-Marie Nicolas
      Pages 3-22

    3. A Logic-Based Calculus of Events

      • Robert Kowalski, Marek Sergot
      Pages 23-55

    4. Metalanguage and Databases

      • Enrico Motta, Maria Simi, Giuseppe Attardi
      Pages 57-72

    5. Efficient Representation of Incomplete Information About Structured Objects

      • Robert Demolombe, Luis Fariñas del Cerro
      Pages 73-89

    6. Abstraction and Inference Mechanisms for Knowledge Representation

      • Amilcar Sernadas, Cristina Sernadas
      Pages 91-116

  3. From Data to Facts and Rules

    1. Front Matter

      Pages 117-117
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    2. How to Look at Deductive Databases

      • Hervé Gallaire, Jean-Marie Nicolas
      Pages 119-130

    3. Extending a Relational DBMS Towards a Rule-Based System: An Approach Using Predicate Transition Nets

      • G. Gardarin, C. de Maindreville, E. Simon
      Pages 131-152

    4. Integrated Fact and Rule Management Based on Relational Technology

      • Joachim W. Schmidt, Lingyuan Ge, Volker Linnemann, Matthias Jarke
      Pages 153-178

    5. Adding a Closure Operator to the Extended Relational Algebra: A Further Step Towards the Integration of Database Techniques and Logic Programming

      • L. Aiello, C. Cecchi
      Pages 179-202

  4. Architectural Issues in Data and Knowledge Base Integration

    1. Front Matter

      Pages 203-203
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    2. Database Management: A Survey

      • Michael L. Brodie, Frank Manola
      Pages 205-240

    3. Towards Databases for Knowledge Representation

      • Erich J. Neuhold, Michael Schrefl
      Pages 241-257

    4. Large-Scale Knowledge Systems

      • John Miles Smith
      Pages 259-281

    5. Issues in Data Base and Knowledge Base Integration

      • Antonio Albano, Giuseppe Attardi
      Pages 283-291

    6. Design of a Compiler for a Semantic Data Model

      • Brian A. Nixon, K. Lawrence Chung, David Lauzon, Alex Borgida, John Mylopoulos, Martin Stanley
      Pages 293-343

    7. Two-Mode Evaluation for Dealing with Implicit Interactions Between Logic Programs and Relational Data Bases

      • Istvan P. Orci, Dan Sahlin
      Pages 345-371

    8. Knowledge Base Management Systems: A Database View

      • Janis A. Bubenko, Istvan P. Orci
      Pages 373-378
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Keywords

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.

Editors and Affiliations

  • Fachbereich Informatik, Johann Wolfgang Goethe-Universität, Frankfurt a. M. 11, Rep. Fed. of Germany

    Joachim W. Schmidt

  • Istituto di Elaborazione della Informazione, Consiglia Nazionale delle Ricerche (CNR), Pisa, Italy

    Constantino Thanos

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