Аннотация
One of the traditional methods for community detection in knowledge graphs is agglomerative clustering. Agglom-erative hierarchical clustering is a widely used type of hierarchical clustering for grouping objects based on their similarity. This method follows a bottom-up approach, beginning with each individual data point considered as an independent cluster, which are then continuously merged based on a similarity threshold between clusters. This paper focuses on the use of agglomerative clustering for analyzing skills extracted from job postings on an online recruitment platform. It describes the approach to data collection, processing, and subsequent clustering, providing an overview of linkage methods between clusters and examples of the application of various coefficients for quantitative assessment of cluster quality. An analysis of bilingual clusters in Russian and English is conducted, al-lowing for an evaluation of the versatility and adaptability of the proposed approach to analyzing the multilingual labor market in Kazakhstan. It was found that agglomerative clustering methods hold significant potential for identi-fying structured groups of skills, which can enhance the understanding of labor market trends and needs. The analysis of clusters formed in different languages confirmed the universality and adaptability of the proposed ap-proach to multilingual data.