{'fraction_non_alphanumeric': 0.019711902956785442, 'fraction_numerical': 0.0, 'mean_word_length': 5.209411764705882, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Ontology Model and Ontological Graph for Development of Decision Support System of Personal Socialization by Common Relevant Interests

Today, due to the extended worldwide pandemic, socialization of persons with same interests is an enormously crucial step in isolating people. Moreover, most individuals are always attempting to simplify and automate all of the essential everyday routines that use a significant amount of free time. The same may be said about the individual's socialization process. In the situation of decision support system (DSS) development and large data analysis, machine learning and SEO technologies are presently increasingly significant. Almost any DSS that has a substantial user base employs proper socializing method. In this example, a unique algorithm based on Levenstein's algorithm, sample extension, N-grams, and the Noisy Channel model was developed. Based on current Levenstein algorithms, sample expansion, N-grams, and the Noisy Channel model, the researchers developed a new algorithm for assessing user information and determining the most apposite IP users based on the inspected text of profile messages. A active socialization DSS was created using an asynchronous programming framework. The convolutional neural network was upgraded, allowing for more effective searching for human faces in photos and checking for existent persons in the DSS database. The DSS will enable efficient and quick text data selection, analysis, processing, and final result generation. For systematic and high-quality intelligent search and processing of applicable information for the needs of a specific user, the DSS employs SEO technologies. By using a neural network, you may accurately identify a user based on his photo. The methods employed in general allow you to develop a convenient DSS socialization employing the relevant techniques. It is worth mentioning the importance of optimizing the current DSS; first and foremost, it is total asynchrony of system, which will eliminate any long waits and difficulties in processing and analyzing requests; second, the system allows efficient and active work with various volumes of large data. DSS users require more data. We also use the cloud platform, which allows for data dispersion. For example, all of the most challenging data may be stored in the cloud environment, and all of the necessary data can be downloaded using a simple basic DSS interface with data queries. As a result, it can be claimed that the development of this DSS is critical both in terms of societal impact and in terms of executing all of the algorithms that the DSS requires.