Approaches to Scaling Skill Intelligence Platforms

Authors

  • Syrotenko Viktoriia LLC Temabit

DOI:

https://doi.org/10.47941/hrlj.3446

Keywords:

Skills-Based Hr, Kill Intelligence Platforms, Skill Extraction, Ontology Alignment, Esco/O*Net, Knowledge Graphs, Large Language Models (LLMs)

Abstract

Purpose: Skill Intelligence Platform (SIP) Support for hiring skills, mobility and learning. This conceptual document synthesizes empirical conclusions to identify SIP scalable and responsible approaches.

Methodology: We have made an integrated overview of fifteen sources with skill extraction, oncology leveling, knowledge of knowledge and operation results. Studies have been carried out for HR relevance and clear data report, matrix and references. The evidence was coded on the map of evidence regarding data sources, algorithms, oncology, evaluation and life cycle control.

Findings: Synthesis created five -emental structure (skill -5): Source integration, knowledge modeling, pipe estimates, life cycle engineering and logic and management. During a study, a hybrid pipe increases the pipeline combining weak supervision with large language models without losing accuracy. Improved portability ESCO/O*improved. The job skill chart allows comparison, mobility and course design. The Lups controls check including the shadow test, Canary and drift monitors. The main risks included distortion, whirling of the concept, drift of tongue and cost instability, Human loop drugs, audit trails and budget route reduced them.

Unique Contribution to Theory, Practice and Policy: This study advances theory by transferring oncology-derived cumulative uncertainty modelling (CUM) and modular hybrid estimation into the HR analytics domain, improves practice by providing a scalable and disciplined framework for evaluating strategic intervention programmes (SIP) against measurable business outcomes, and informs policy by recommending standardized research intervals, multilingual benchmarking, automated evidence updates, and mandatory distortion audits to align analytical models with real workforce effects.

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References

Chiarello, F., Fantoni, G., Hogarth, T., Giordano, V., Baltina, L., & Spada, I. (2021).

Towards ESCO 4.0: Is the European classification of skills in line with Industry 4.0? A text mining approach. Technological Forecasting and Social Change, 173, 121177. https://doi.org/10.1016/j.techfore.2021.121177

Decorte, J.-J., Van Hautte, J., Deleu, J., Develder, C., & Demeester, T. (2022). Design of negative sampling strategies for distantly supervised skill extraction. In Proceedings of the RecSys in HR 2022 Workshop (CEUR Workshop Proceedings, Vol. 3218). https://ceur-ws.org/Vol-3218/RecSysHR2022-paper_4.pdf

de Groot, M., Verberne, S., & Arendsen, R. (2021). Job posting-enriched knowledge graph for skills-based education and job matching. arXiv. https://arxiv.org/pdf/2109.02554

Gavrilescu, M., Dinsoreanu, M., & Rădulescu, C. (2025). Techniques for transversal skill classification and extraction based on ESCO using AI. Information, 16(3), 167. https://www.mdpi.com/2078-2489/16/3/167

Howison, M., Ensor, W. O., Paden, M., & Walker, K. (2024). Extracting structured labor market information from job postings using generative AI. Digital Government: Research and Practice. https://doi.org/10.1145/3674847

Nguyen, K. C., Zhang, M., Montariol, S., & Bosselut, A. (2024). Rethinking skill extraction in the job market domain using large language models. In Proceedings of the 1st Workshop on NLP for Human Resources (NLP4HR 2024). Association for Computational Linguistics. https://aclanthology.org/2024.nlp4hr-1.3.pdf

OECD. (2024). Artificial intelligence and the changing demand for skills in the labour market. OECD Publishing. https://www.oecd-ilibrary.org/education/artificial-intelligence-and-the-changing-demand-for-skills-in-the-labour-market_2a240956-en

Ortega-Flores, I., Casado-Lumbreras, C., Colomo-Palacios, R., & Soto-Aguirre, M. (2022). Using LinkedIn endorsements to reinforce an ontology and ML-based recommender system to improve professional skills. Electronics, 11(8), 1190. https://www.mdpi.com/2079-9292/11/8/1190

Rosenberger, J. S., Canzani, E., Marcon, L., Baeza-Yates, R., & Gionis, A. (2025). CareerBERT: Matching résumés to ESCO jobs in a shared embedding space. Expert Systems with Applications. Advance online publication. https://www.sciencedirect.com/science/article/pii/S0957417425006657

Seif, A., Fadel, M., El Bassuony, A., & El-Beltagy, S. (2024). A dynamic jobs-skills knowledge graph. In Proceedings of the RecSys in HR 2024 Workshop (CEUR Workshop Proceedings, Vol. 3788). https://ceur-ws.org/Vol-3788/RecSysHR2024-paper_1.pdf

Senger, E., Zhang, M., van der Goot, R., & Plank, B. (2024). A survey on skill extraction and classification from job postings. In Proceedings of the 1st Workshop on NLP for Human Resources (NLP4HR 2024). Association for Computational Linguistics. https://aclanthology.org/2024.nlp4hr-1.1.pdf

Shi, B., Yang, J., Guo, F., & He, Q. (2020). Salience and market-aware skill extraction for job targeting. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2954–2964). https://doi.org/10.1145/3394486.3403338

Vrolijk, J., Rebelo, P., Beijer, H., & van der Meer, R. (2024). Ontology learning for ESCO: Leveraging LLMs to navigate the skills graph. In Joint Ontology Workshops 2024 (CEUR Workshop Proceedings). http://ceur-ws.org/Vol-3829/short3.pdf

Zhang, M., Jensen, K. N., Sonniks, S. D., & Plank, B. (2022). SkillSpan: Hard and soft skill extraction from English job postings. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2022). https://aclanthology.org/2022.naacl-main.366.pdf

Zhang, M., Jensen, K. N., van der Goot, R., & Plank, B. (2022). Skill extraction from job postings using weak supervision. In Proceedings of the RecSys in HR 2022 Workshop (CEUR Workshop Proceedings, Vol. 3218). https://vbn.aau.dk/files/662796103/RecSysHR2022_paper_10.pdf

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Published

2026-01-15

How to Cite

Viktoriia, S. (2026). Approaches to Scaling Skill Intelligence Platforms. Human Resource and Leadership Journal, 11(1), 1–13. https://doi.org/10.47941/hrlj.3446

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Section

Articles