Rule Induction for Semantic Query Optimization

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. Experimentalresults demonstrate that this approach can learn su cient background knowledge to reformulate queries and provide a 57percent average performance improvement.

• Type: Conference
• Year: 1994
• Where: KDD
• Authors: Chun-Nan Hsu, Craig A. Knoblock
• Keywords: Data Mining, Inductive Learning Algorithm, KDD 1994, Semantic Query Optimization, Semantic Rules