Management information systems: conceptual foundations, structure, and development (2nd ed.)
Management information systems: conceptual foundations, structure, and development (2nd ed.)
Multifacetted modelling and discrete event simulation
Multifacetted modelling and discrete event simulation
Knowledge representation from Newton to Minsky and beyond
Applied Artificial Intelligence
Merging expert systems and databases
AI Expert
Entity Structure Based Design Methodology: A LAN Protocol Example
IEEE Transactions on Software Engineering
End user logical database design: the structured entity model approach
End user logical database design: the structured entity model approach
Methodical design and maintenance of well structured rule base
SIGBDP '90 Proceedings of the 1990 ACM SIGBDP conference on Trends and directions in expert systems
An object-oriented methodology for knowledge base/database coupling
Communications of the ACM
A Knowledge-Based Fatal Incident Decision Model
IEEE Transactions on Knowledge and Data Engineering
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Advantages of a coupled database/knowledgebase (DB/KB) have been widely recognized by both researchers and practitioners. The issue addressed in this paper is how to achieve a natural and effective DB/KB coupling. A major weakness of a typical database system is its lack of semantic support. A knowledge-based system can provide semantic support to a database system. On the other hand, development of a large effective knowlgedebase is difficult because of its limited capacity to and maintain factual data. A database system can help a knowledge-based system overcome this weakness. Thus DB/KB coupling enhances functionality of both participating database systems and knowledge-based systems. The Structured Entity Model (SEM) method, an object-oriented methodology which initially was developed as a logical database design, is capable of accommodating a natural and effective DB/KB coupling design. SEM is derived from System Entity Structure, which is a hierarchical knowledge representation for simulation modeling. Because of the hierarchical structure and decomposition constructs of SEM, it can help a designer perform top-down structured analysis and design of databases. SEM also achieves a high level of semantic expressiveness by using a frame representation for entities and three general association categories (aspect, specialization, and multiple decomposition) for relationships. Update and query constraints and other semantics can be stored in each frame. This also enables SEM to have high potential as a knowledge representation scheme for a knowledgebase. Since both database schema and knowledge structure are produced from the same SEM structure, they are naturally coupled through a common SEM structure. In this paper, the use of SEM as a methodology for producing a coupled DB/KB is described. SEM is implemented using Nexpert system This implementation is illustrated through a DB/KB coupling example based on an Integrated Office Information System (IOIS)