Knowledge-Based Learning: Integration of Deductive and Inductive Learning for Knowledge Base Completion.

In constructing a knowledge-based system, the knowledge engineer must convert rules of thumb provided by the domain expert and previously solved examples into a working system. Research in machine learning has produced algorithms that create rules for knowledge-based systems, but these algorithms require either many examples or a complete domain theory (often in the form of rules). In many real-world situations, only a limited number of examples and an incomplete domain theory are available. This thesis presents a new paradigm for machine learning--knowledge-based learning (KBL)--which combines the strengths of empirical learning with the strengths of explanation-based learning to overcome previous limitations. Two systems--KBL0 and KBL1--are used to illustrate the new paradigm. These systems have been designed and implemented to work with domains requiring a representation of real numbers and mathematical formulas, such as engineering. This research has shown not only that it is possible to use a domain theory to guide induction using examples, but that when there are few examples available compared to the size of the problem space, the resulting rules are more accurate and stable than those from pure empirical techniques. In addition, knowledge-based algorithms free the user from selecting relevant examples and attributes for learning by using an incomplete domain theory to determine where knowledge needs to be added. A problem unsolved by the current domain knowledge helps to determine where new knowledge needs to be incorporated into the domain theory and what the context is for the learning. The context is used to select relevant examples from an example base and to reduce the number of attributes needed during the induction. With the control structure provided by knowledge-based systems, inductive learning can be used to extend an existing knowledge base. (69 references) (Author/DB)