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In the field of medical knowledge engineering, it is a common expectation that the number of diseases contained within a given system should constantly increase. The authors' effort to develop an enormous knowledge-based system (the Enormous Electronic-Brain Erudite, EBME) has extended for more than 10 years. The reason for such a long time-frame is that EBME has a huge knowledge base that consists of 1,001 diagnostic entities. It is not only time consuming and tedious but also very error-prone to build this type of database manually. To overcome this problem of time and accuracy, we put forward an assembly technique for knowledge-based systems, which we describe in this article. Our research direction is to develop a methodology to build an enormous knowledge-based system. The goals of this study are: (1) to enhance the efficiency of knowledge engineering by automating the knowledge engineering processes (2) to avoid repeated labor as much as possible (3) in an enormous knowledge-based system, to assemble different subsystems that not only meet the different needs of different users, but also are useful to avoid the occurrence of "combination explosion" (4) to advance the research of medical information processing standardization.