kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университет10.55452/1998-6688-2026-23-3-312-327kaz29-3194Research ArticleКОМПЬЮТЕРНЫЕ НАУКИCOMPUTER SCIENCEМОДЕЛИ ПОСТРОЕНИЯ ГРАФОВ ЗНАНИЙ ДЛЯ ПОДБОРА МООК НА ОСНОВЕ ИНТЕГРАЦИИ ЗАПРОСОВ РЫНКА ТРУДА И ОБРАЗОВАТЕЛЬНЫХ ПРОГРАММBUILDING KNOWLEDGE GRAPHS FOR MOOC SELECTION BASED ON THE INTEGRATION OF LABOR MARKET DEMANDS AND EDUCATIONAL PROGRAMShttps://orcid.org/0000-0002-3627-3321СерикбаеваС. К.SerikbayevaK. S.

PhD, И.о.доцента

Астана

PhD, Acting Associate Professor

Astana

inf_8585@mail.ruhttps://orcid.org/0000-0001-6100-3123РамазановаВ. С.RamazanovaV. S.

Магистр, главный инженер

Уральск

Master’s degree, Chief Engineer

Ural

ramazanovavs@gmail.comhttps://orcid.org/0000-0001-9358-1614СамбетбаеваМ. А.SambetbayevaM. A.

Phd, ассоциированный профессор

Астана

PhD, associate professor

Astana

sambetbayeva_ma_1@enu.kzhttps://orcid.org/0000-0002-2013-1513ЕримбетоваА. С.YerimbetovaА.

PhD, к.т.н., ассоциированный профессор, профессор-исследователь, в.н.с.

Алматы

Cand. Eng. Sci., Associate Professor, professor-researcher, Leading Researcher

Almaty

aigerian8888@gmail.comhttps://orcid.org/0000-0002-7514-9315СадирмековаЖ. Б.SadirmekovaZh.

PhD, ассоциированный профессор

Астана

PhD, Associate Professor

Astana

Zhanna.Sadirmekova1@astanait.edu.kzЕвразийский национальный университет им. Л.Н. ГумилеваКазахстанL.N. Gumilyov Eurasian National UniversityKazakhstanЗападно-Казахстанский университет им. М. УтемисоваКазахстанM. Utemisov West Kazakhstan UniversityKazakhstanMETA University; Институт информационных и вычислительных технологий КН МНВО РККазахстанMETA University; Institute of Information and Computational Technologies CS MSHE RKKazakhstanAstana IT UniversityКазахстанAstana IT UniversityKazakhstan202626092026233312327Copyright © Серикбаева С.К., Рамазанова В.С., Самбетбаева М.А., Еримбетова А.С., Садирмекова Ж.Б., 20262026Серикбаева С.К., Рамазанова В.С., Самбетбаева М.А., Еримбетова А.С., Садирмекова Ж.Б.Serikbayeva K.S., Ramazanova V.S., Sambetbayeva M.A., Yerimbetova А., Sadirmekova Z.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/3194

В статье рассматривается разработка интеграционной модели на основе графов знаний и методов машинного обучения для анализа взаимосвязей между образовательными программами, требованиями рынка труда и онлайн-курсами (MOOC). Актуальность исследования обусловлена быстрыми изменениями в сфере информационных технологий, приводящими к несоответствию между подготовкой выпускников и ожиданиями работодателей. Для устранения данного разрыва предложено использование гетерогенного графа знаний, объединяющего данные из трех доменов: университетских программ, вакансий и онлайн-курсов. В модели реализован мета-путь, связывающий навыки, требования вакансий и образовательные курсы, что позволяет формировать рекомендации по обучению и карьерному развитию для различных категорий пользователей. Для обработки текстовых данных применены мультиязычные трансформеры, обеспечивающие анализ русскоязычных и англоязычных описаний. В качестве источников данных использованы карты компетенций образовательных программ, вакансии платформы hh.kz и курсы Coursera, интегрированные в графовую СУБД Neo4j. Полученные результаты демонстрируют возможность выявления недостающих компетенций и автоматического подбора курсов для их формирования. Предложенная модель может применяться при проектировании учебных программ, разработке образовательных траекторий и поддержке принятия решений в образовательной среде.

The article presents the development of an integration model based on knowledge graphs and machine learning methods for analyzing relationships between educational programs, labor market requirements, and online courses (MOOCs). The relevance of the study is driven by rapid changes in the information technology sector, which lead to a mismatch between graduate competencies and employer expectations. To reduce this gap, a heterogeneous knowledge graph integrating data from university curricula, job vacancies, and online courses is proposed. The model implements a meta-path connecting skills, job requirements, and learning courses, enabling recommendations for educational and career development for different categories of users. Multilingual transformers were applied to process textual descriptions in Russian and English. The data sources include competency maps of educational programs, job postings from the hh.kz platform, and courses from Coursera, all integrated into the Neo4j graph database. The obtained results demonstrate the capability to identify missing competencies and automatically recommend relevant courses for their acquisition. The proposed model can be applied in curriculum design, development of learning trajectories, and decision-making support within educational environments.

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The authors declare that there are no conflicts of interest present.