This article presents the development and testing of a methodology for building an ontological profile designed for content personalization systems. It details the modular architecture of a web-based personalization system, illustrating the text processing and analysis methods and algorithms employed at each stage, and provides a step-by-step procedure for ontology creation. The methodology encompasses primary data processing, including the extraction of keywords and phrases, followed by their hierarchical clustering to reveal the semantic structure of the domain. Subsequent stages involve defining thresholds to filter out insignificant connections, and extracting and formalizing relationships between concepts using natural language processing techniques such as word-sense disambiguation and semantic similarity-based relationship extraction. An integrated pipeline was developed to implement this process, combining improved algorithms proposed by the author in previous studies, namely, an algorithm for extracting key phrases from individual text based on semantic similarity and a modified algorithm for word sense disambiguation. This pipeline also optimally integrated all necessary natural language processing tools, ensuring the efficient operation of these methods in the process of automatically constructing an ontology from text. The study places particular emphasis on a comprehensive evaluation of the resulting ontology using a specialized set of criteria designed to objectively assess the profile's quality, completeness, and consistency. A important component of the work is a computational experiment that clearly demonstrates the impact of each data processing stage on the final quality and efficacy of the ontology. The results show that the proposed method enables the construction of a practical, scalable, and relevant ontology, suitable for industrial deployment and integration into personalization systems to enhance their accuracy and adaptability
