Systems with the capability of automatic knowledge discovery from databases will play an increasingly important role in building/sharing large knowledge bases. Although many systems for knowledge discovery in databases have been proposed, few of them have addressed the capabilities of refining/managing the discovered knowledge. In particular, 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. This paper describes a way of managing and refining structural characteristics discovered from databases by using the IIBR (Inheritance Inference Based Refinement) subsystem of our GLS (Global Learning Scheme) discovery system, and it can be cooperatively used with other subsystems of GLS, such as KOSI (Knowledge Oriented Statistic 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 the deductive rules and the sets of data for showing its error, and can be managed and refined easily. IIBR is based on inheritance inference and error analysis, as well as the model representation of knowledge in the knowledge-based system KAUS. Experience with a prototype of IIBR implemented by KAUS is discussed.