KNOWLEDGE REPRESENTATION FOR GROUNDED THEORY CONSTRUCTION IN QUALITATIVE DATA ANALYSIS.

The major achievement of most current qualitative data analysis software 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 research. Application of knowledge-based systems has been recognised as an important approach to theory construction in qualitative data analysis. This approach heavily depends on a suitable way of knowledge representation. This paper describes a knowledge representation method for representing grounded theory construction, in which a hybrid approach of fuzzy set theory and semantic networks is applied. [ABSTRACT FROM AUTHOR]