Using inductive learning to generate rules for semantic query optimization
Chun‐Nan Hsu, Craig A. Knoblock · 1996
Semantic query optimization can dramatically speed up database query answering byknowledge 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 effective for optimization because they will match query patterns and reflect data regularities. Experimental results show that this approach learns sufficient rules for optimization that produces a substantial cost reduction.