Intelligent query answering with data mining techniques

Data mining in databases is an important issue in the development of data and knowledge-base systems. It facilitates querying database knowledge and semantic query optimization. The aim of the work is to use data mining tools for intelligent query answering in database systems, which include generalization, data summarization and rule discovery. We used a model for a knowledge-rich database, which consists not only of the components from a deductive database but also the components relevant to knowledge discovery tools. The discovered rule set constitutes a graph whose edges are the rules. This condition dependency graph provides a map of possible query reformulation operations so that semantic query optimization could be performed. Semantic knowledge is needed to optimize queries. Semantic knowledge may be in the form of generalized rules or association rules. Both will be applied to semantic query optimization system.