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

Abstract

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 ming and knowledge processing. In the paper, we present a method for exacting 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 semanticpreserving, 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.

Text-Based Intent Analysis using Deep Learning

“…Zhonghe Gao [16] used a concept of conceptual lattices which is semantic in nature and is used to look for relations between different contexts and analyze the text or data accordingly. Usage of concept-ual lattice allows to fetch more dense and richer intents as it is not only semantic preserving but also facilitates dimension addition.…”