Building knowledge base management systems

  • Authors:
  • John Mylopoulos;Vinay Chaudhri;Dimitris Plexousakis;Adel Shrufi;Thodoros Topologlou

  • Affiliations:
  • Department of Computer Science, University of Toronto, 6 King's College Road, Toronto, Canada M5S 1A4;SRI International, Menlo Park, CA 94025, USA;Department of Computing and Information Sciences, Kansas State University, Manhattan, KS 66506, USA;Department of Computer Science, University of Toronto, 6 King's College Road, Toronto, Canada M5S 1A4;Department of Computer Science, University of Toronto, 6 King's College Road, Toronto, Canada M5S 1A4

  • Venue:
  • The VLDB Journal — The International Journal on Very Large Data Bases
  • Year:
  • 1996

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Abstract

Advanced applications in fields such as CAD, software engineering, real-time process control, corporate repositories and digital libraries require the construction, efficient access and management of large, shared knowledge bases. Such knowledge bases cannot be built using existing tools such as expert system shells, because these do not scale up, nor can they be built in terms of existing database technology, because such technology does not support the rich representational structure and inference mechanisms required for knowledge-based systems. This paper proposes a generic architecture for a knowledge base management system intended for such applications. The architecture assumes an object-oriented knowledge representation language with an assertional sublanguage used to express constraints and rules. It also provides for general-purpose deductive inference and special-purpose temporal reasoning. Results reported in the paper address several knowledge base management issues. For storage management, a new method is proposed for generating a logical schema for a given knowledge base. Query processing algorithms are offered for semantic and physical query optimization, along with an enhanced cost model for query cost estimation. On concurrency control, the paper describes a novel concurrency control policy which takes advantage of knowledge base structure and is shown to outperform two-phase locking for highly structured knowledge bases and update-intensive transactions. Finally, algorithms for compilation and efficient processing of constraints and rules during knowledge base operations are described. The paper describes original results, including novel data structures and algorithms, as well as preliminary performance evaluation data. Based on these results, we conclude that knowledge base management systems which can accommodate large knowledge bases are feasible.