Abstract
Knowledge-base management systems (KBMS) based on description logics are being used in a variety of situations where access is needed to large amounts of data stored in existing relational databases. We present the architecture and algorithms of a system that converts most of the inferences made by the KBMS into a collection of SQL queries, thereby relying on the optimization facilities of existing DBMS to gain efficiency, while maintaining an object-centered view of the world with a substantive semantics and significantly different reasoning facilities than those provided by Relational DBMS and their deductive extensions. We address a number of optimization issues that arise in the translation process due to the fact that SQL queries with different syntax (but identical semantics) are not treated uniformly by current database management systems.
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