An Approach to Text Knowledge Analysis Based on Many-Valued Context

Text knowledge analysis plays a very important role in knowledge processing. In order to find interesting patters, we introduce text analysis into the formal concept analysis(FCA) field. FCA is a valid tool for data mining and knowledge processing. In the paper, we present a method for extracting the conceptual patterns and concept lattice from multiple knowledge sources. The method consists of two steps: the first is to insert some related many-valued contexts (MVCs) (which represent text knowledge) into some corresponding column or cell of other MVC in accordance with certain rules, and then extract formal concepts and concept lattice. Our insertion method not only is semantic-preserving, but adds the dimension of attributes for describing objects. Hence, the discovered concepts have the more richer intents and are more denser in the concept lattice. The paper mainly applied the method to column insertion operation and cell insertion operation, and obtained some propositions, which can be used by knowledge engineers to represent and analyze text knowledge (especially, text objects) and to extract new semantics information or associate rules from concept lattices.