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        <title>CareerPathKG: Knowledge Graph Integrated Framework for Career Intelligence</title>
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            <namePart type="given">Yevgen</namePart>
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    <abstract>The labor market is experiencing rapid and continual shifts in required skills and competencies, driven by technological advancement and evolving industry structures. Within this dynamic environment, candidates increasingly face challenges in orienting their career development, requiring them to continuously update their knowledge and capabilities to meet contemporary job requirements; this need is particularly necessary for new entrants to the labor market, who must cultivate a comprehensive understanding of current labor-market conditions. To address these issues, this study proposes an enterprise recruitment framework grounded in a career path knowledge graph, capturing occupations, skill requirements, and career transitions using standardized taxonomies enriched with job-posting data. The framework integrates transformer-based embeddings, large language models, and knowledge-graph reasoning to support efficient and reliable CV assessment, CV-JD matching and career guidance.</abstract>
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%0 Conference Proceedings
%T CareerPathKG: Knowledge Graph Integrated Framework for Career Intelligence
%A Le, Ngoc-Quang
%A Hoang, Duc Duong
%A Tran, Mai Vu
%A Vuong, Thi-Hai-Yen
%Y Matusevych, Yevgen
%Y Eryiğit, Gülşen
%Y Aletras, Nikolaos
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-384-5
%F le-etal-2026-careerpathkg
%X The labor market is experiencing rapid and continual shifts in required skills and competencies, driven by technological advancement and evolving industry structures. Within this dynamic environment, candidates increasingly face challenges in orienting their career development, requiring them to continuously update their knowledge and capabilities to meet contemporary job requirements; this need is particularly necessary for new entrants to the labor market, who must cultivate a comprehensive understanding of current labor-market conditions. To address these issues, this study proposes an enterprise recruitment framework grounded in a career path knowledge graph, capturing occupations, skill requirements, and career transitions using standardized taxonomies enriched with job-posting data. The framework integrates transformer-based embeddings, large language models, and knowledge-graph reasoning to support efficient and reliable CV assessment, CV-JD matching and career guidance.
%U https://aclanthology.org/2026.eacl-industry.60/
%P 813-822

Markdown (Informal)

[CareerPathKG: Knowledge Graph Integrated Framework for Career Intelligence](https://aclanthology.org/2026.eacl-industry.60/) (Le et al., EACL 2026)

• CareerPathKG: Knowledge Graph Integrated Framework for Career Intelligence (Le et al., EACL 2026)

ACL

• Ngoc-Quang Le, Duc Duong Hoang, Mai Vu Tran, and Thi-Hai-Yen Vuong. 2026. CareerPathKG: Knowledge Graph Integrated Framework for Career Intelligence. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track), pages 813–822, Rabat, Morocco. Association for Computational Linguistics.