Papers by Author: Ting Zhong Wang

Abstract: The major content in FCA is to extract formal concepts and connections between them from data in form of formal context so as to form a lattice structure of formal concepts. Fuzzy set theory and fuzzy logic are acknowledged as an appropriate formalism for capturing imprecise and vague knowledge. The paper offers a methodology for building ontology for knowledge sharing and reusing based on fuzzy concept lattices union. This paper makes up these defects by applying formal concept analysis theory and fuzzy sets to construct concept hierarchies of ontology, and the experiments shows the CPU Time in the attribute numbers, indicating that FFCA is superior to FCA in building the ontology of semantic web.

Abstract: Formal concept lattices and rough set theory are two kinds of complementary mathematical tools for data analysis and data processing. The algorithm of concept lattice reduction based on variable precision rough set is proposed by combining the algorithms of β-upper and lower distribution reduction in variable precision rough set. The traditional algorithms aboutβvalue select algorithm, attribute reduction based on discernibility matrix and extraction rule in VPRS are discussed, there are defects in these traditional algorithms which are improved. Finally, the generation system of concept lattice based on variable precision rough set is designed to verify the validity of the improved algorithm and a case demonstrates the whole process of concept lattice construction.