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Research on Knowledge Representation and Reasoning Based on Decision Implication
Abstract
Knowledge representation and reasoning based on decision implication is to perform an extremely compact representation of the decision information implied in data, and obtain all decision information in data by means of reasoning, based on the current representation. The existing research in logic and data aspects of decision implication proposes a set of decision implications with information integrity and extreme simplicity (non-redundancy and optimality), i.e., decision implication canonical basis (DICB), which lays a solid foundation for constructing a knowledge representation and reasoning framework in formal contexts. This chapter conducts a systematic and in-depth study on the important issues of knowledge representation capability, and incomplete formal context adaptability of decision implication, and reasoning based on decision implications.
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