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Formal Concept Analysis (FCA) is a rigorous mathematical theory in the “Data mining” research field. It advances the classical approach to the Concept as to a fundamental epistemic element which is determined by extent and intent. FCA is suitable for mining formal ontologies from the experimental data representing Domains of Interest (DI). In this sense Fuzzy FCA (FFCA) is an adaptation of the FCA to real nature of such information. The genesis study of fuzziness of the formal contexts is a new approach, which necessitates the inclusion of special stages of primary data processing into designing ontologies. It is shown that some of the reasons for this fuzziness are inherent in the technology of generating a formal context from experimental data. Other fuzziness factors were revealed during the morphological analysis of the basic empirical structure the "objects-properties" table. Interpretation of additional information is possible on the basis of elementary methods of fuzzy inference. Lastly, variants of FFCA application for fuzzy ontologies are analyzed.

FUZZY FORMAL CONCEPT ANALYSIS IN THE CONSTRUCTION OF ONTOLOGIES