Inference Method of Deductive Database Based on Relational Database

Abstract

For Knowledge Information Processing Systems in Expert-Systems or CAD Systems, it is important to handle massive knowledge efficiently. For this purpose, it is feasible to use Relational Database to construct Knowledge Information Processing Systems. But a mere connection of a Prolog Machine with a Relational Database is insufficient because of its communication overhead between the two elements. We take integration-approach ; enhancement of Relational Databases with a facility of unification. To implement resolution for deductive databases based on integration-approach, it is important to extend relations so as to represent terms, and to enhance relational algebra with unification power. We develop EXTRA ; extended relational algebra with unification power, and a resolution algorithm using EXTRA. Compared with conventional algorithms, our algorithm is more efficient in some cases. We verified this by counting processing steps by number of tuples processed. To make our resolution algorithm more efficient, some facilities must be added. One is to evaluate goals according to the informations about the goals. We found it is effective to evaluate goals according to the types of definitions and numbers of definitions for the goals. Another facility is to use EXTRA sequences instead of resolution. It is effective when the no recursion is involved. We will integrate these facilities into one algorithm. To do this, some preprocessing must be needed. We also study programming environment of deductive databases for expert systems, and architecture to support extended relational algebra. We will integrate the results of these studies to make up a deductive database based on relational algebra.