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Recently, researchers have been developing methods to create fuzzy concept lattices from fuzzy formal contexts. These fuzzy concept lattices then serve as the basis for creating fuzzy ontologies which allow uncertain knowledge to be represented in ontology. This thesis research investigates two current approaches to create fuzzy concept lattices: the alpha-cut method and the fuzzy closure method, both of which have been implemented as part of this thesis research. Experimentation with the fuzzy closure method revealed that it produces an enormous number of formal concepts. A method to determine the more significant formal concepts within such huge fuzzy concept lattices is implemented using a factor analysis approach. The implementations are tested first with simple example fuzzy formal contexts. Then using the two approaches fuzzy concept lattices are created using the Gene Ontology and gene annotation data file. These two approaches to fuzzy concept lattice creation are then compared based on the results of these experiments. The factor analysis method is also applied to the various fuzzy formal contexts and the results analyzed with respect to those produced by the alpha-cut and the fuzzy closure methods. In addition, extensions to allow for fuzzy querying over the fuzzy concept lattices are implemented as part of the thesis research.