Using Metagueries to Integrate Inductive Learning and Deductive Database Technology

This paper presents an approach that uses metaqueries to integrate inductive learning with deductive database technology in the context of knowledge discovery from databases. Metaqueries are second-order predicates or templates, and are used for (1) Guiding deductive data collection, (2) Focusing attention for inductive learning, and (3) Assisting human analysts the discovery loop. We describe in detail a system that uses this idea to unify a Bayesian Data Cluster with the Logical Data Language (LDL++), and show the results of three case studies, namely: discovering regularities from a knowledge base, discovering patterns and errors from a large telecommunication database, and discovering patterns and errors from a large chemical database.