KNOWLEDGE REPRESENTATION FOR GROUNDED THEORY CONSTRUCTION IN QUALITATIVE DATA-ANALYSIS

The major achievement of most current qualitative data analysis softwa re systems in social sciences has been the efficient code-and-retrieve abilities. Although such abilities greatly strengthen and assist the handling of qualitative data, they do not address the crucial tasks of theory construction as traditionally understood in qualitative resear ch. Application of knowledge-based systems has been recognised as an i mportant approach to theory construction in qualitative data analysis. This approach heavily depends on a suitable way of knowledge represen tation. This paper describes a knowledge representation method for rep resenting grounded theory construction, in which a hybrid approach of fuzzy set theory and semantic networks is applied.