IIBR-a system for managing/refining structural characteristics discovered from databases

The contents of most databases are ever changing; and erroneous data can be a significant problem in real-world databases. Hence, the process of discovering knowledge from databases is a process based on incipient hypothesis generation/evaluation and refinement/management. Although many systems for knowledge discovery in databases have been proposed, most systems have not addressed the capabilities of refining/managing the discovered knowledge. This paper describes a system called IIBR (inheritance inference based refinement) for managing/refining the knowledge discovered from databases. IIBR is one of subsystems of the GLS (global learning scheme) discovery system, and can be cooperatively used with other subsystems of GLS such as KOSI (knowledge-oriented statistical inference). By means of IIBR, the structural characteristics denoted by regression models, which are discovered from a database by KOSI, can be added to a knowledge base as deductive rules and data sets for showing the rule errors, and can be managed and refined easily. IIBR is based on inheritance inference and error analysis, as well as the model-based representation of knowledge in the knowledge-based system KAUS