Rule Induction for Semantic Query Optimization

Chun-Nan Hsu and Craig A. Knoblock

Semantic query optimization can dramatically speed up database query answering by knowledge intensive reformulation. But the problem of how to learn required semantic rules has not previously been solved. This paper describes an approach using an inductive learning algorithm to solve the problem. In our approach, learning is triggered by user queries and then the system induces semantic rules from the information in databases. The inductive learning algorithm used in this approach can select an appropriate set of relevant attributes from a potentially huge number of attributes in real-world databases. Experimental results demonstrate that this approach can learn sufficient background knowledge to reformulate queries and provide a 57 percent average performance improverrtent.


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