Abstract

Semantic query optimization can dramatically speed up database query answering by knowledge intensive reformulation. But the problem of how to learn the required semantic rules has not been previously solved. This chapter presents a learning approach to solving this problem. In our approach, the learning is triggered by user queries. Then the system uses an inductive learning algorithm to generate semantic rules. This inductive learning algorithm can automatically select useful join paths and attributes to construct rules from a database with many relations. The learned semantic rules are e ective for optimization because they will match query patterns and re ect data regularities. Experimental results show that this approach learns su cient rules for optimization that produces a substantial cost reduction.

Date

1995

Authors

Craig Knoblock