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.
Series Description.- Preface.- I. Logic and Knowledge Representation.- 1. The Role of Logic for Data and Knowledge Bases: A Brief Survey.- Discussion.- 2. A Logic-Based Calculus of Events.- Discussion.- 3. Metalanguage and Databases.- Discussion.- 4. Efficient Representation of Incomplete Information About Structured Objects.- 5. Abstraction and Inference Mechanisms for Knowledge Representation.- Discussion.- II. From Data to Facts and Rules.- 6. How To Look at Deductive Databases.- Discussion.- 7. Extending a Relational DBMS Towards a Rule-Based System: An Approach Using Predicate Transition Nets.- 8. Integrated Fact and Rule Management Based on Relational Technology.- 9. Adding a Closure Operator to the Extended Relational Algebra: A Further Step Towards the Integration of Database Techniques and Logic Programming.- Discussion.- III. Architectural Issues in Data and Knowledge Base Integration.- 10. Database Management: A Survey.- Discussion.- 11. Towards Databases for Knowledge Representation.- Discussion.- 12. Large-Scale Knowledge Systems.- 13. Issues in Data Base and Knowledge Base Integration.- Discussion.- 14. Design of a Compiler for a Semantic Data Model.- 15. Two-Mode Evaluation for Dealing with Implicit Interactions Between Logic Programs and Relational Data Bases.- Discussion.- 16. Knowledge Base Management Systems: A Database View.- IV. Knowledge Base Management for AI Applications.- 17. KBMS Requirements of Knowledge-Based Systems.- 18. Conceptual Languages: A Comparison of ADAPLEX, Galileo, and Taxis.- Discussion.- 19. The Software Development Environment as a Knowledge Base Management System.- Discussion.- 20. Retrieving Events from Geometrical Descriptions of Time-Varying Scenes.- 21. A Deductive Solution for Plan Generation.- Discussion.- V. Concluding Remarks.- 22. The Limitations of Logic and Its Role in Artificial Intelligence.- Discussion.- 23. The Need for a Knowledge Representation Framework.- 24. DB Ideas for KBMS.- Comments.- 25. On Application-Oriented and Tool-Oriented Theories.- Discussion.- Final Discussion.- References.- Contributors.
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