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The object of this study is the analysis of questionnaire data using ontological modeling. The task relates to the fact that conventional methods for processing questionnaire data are often insufficiently effective when working with large volumes of information and do not make it possible to automate many analysis processes. As a result of the study, an ontology was designed that structures and analyzes questionnaire data, which allows for a more accurate identification of hidden relationships between variables. Using these theoretical provisions, an information system for assessing the quality of assimilation of preschool children's competencies was built. 150 children from various preschool organizations were involved in the study as respondents. The data integration method proposed in this paper significantly facilitated the process of data analysis both for a group and for an individual respondent. The key difference of the proposed methodology is the automation of routine data analysis operations based on the ontological structure, which significantly simplifies the processing of large volumes of information. This makes it possible to solve the problem of limitations in conventional analysis methods and makes data analysis more scalable and reproducible. The practical application of the results is possible in marketing for analyzing customer satisfaction, market segmentation, and evaluating the effectiveness of advertising campaigns. In the educational domain, the ontology could be used to evaluate the quality of programs and analyze respondents' opinions, and in sociology – to analyze public opinion and conduct research on social phenomena. Thus, the proposed ontology provides an effective tool for analyzing large volumes of questionnaire data, allowing organizations to make more informed decisions and improve their efficiency
Iklassova et al. (Wed,) studied this question.