Development of a hybrid information retrieval–knowledge graph model for cross-framework competency alignment

Authors

DOI:

https://doi.org/10.15587/1729-4061.2026.358313

Keywords:

information retrieval, knowledge graph, competency alignment, CPL, SKKNI, O*NET, ESCO, career readiness index

Abstract

The object of this study is the alignment of Indonesian computing graduate learning outcomes (CPL – Capaian Pembelajaran Lulusan) with three heterogeneous competency frameworks: the European skills, competences, qualifications and occupations (ESCO), the occupational information network (O*NET), and the Standar Kompetensi Kerja Nasional Indonesia (SKKNI). The problem addressed is the absence of a unified pipeline capable of simultaneously mapping CPL across these frameworks while accounting for national qualification hierarchies and cross-lingual constraints. An IR-KG (information retrieval–knowledge graph) model is proposed with a seven-stage pipeline using a hybrid scoring function S_final = α·S_sem + β·S_gr + γ·S_con, integrating TF-IDF (term frequency–inverse document frequency) semantic similarity, ESCO knowledge graph cohesion, and domain constraint scores from ISCED-F 2013 (International Standard Classification of Education: Fields of Education and Training) and APTIKOM 2022 (Asosiasi Pendidikan Tinggi Informatika dan Komputer) classifications. The balanced configuration (α = β = γ = 0.33) achieves a mean selection objective of 0.537, a 26.1% improvement over the semantic baseline. External consistency validation yields a relaxed consistency rate of 27.1% (8.7× above random baseline), confirming valid alignment signal capture. The CRI-KG (Career Readiness index based on knowledge graph) reveals a gradient R_SKKNI >> R_ONET > R_ESCO, exposing persistent gaps in international framework coverage. The pipeline is applicable for curriculum audit, qualification recognition policy, and national-to-international framework integration where labelled training data are unavailable

Author Biographies

Halim Maulana, Universitas Sumatera Utara

Doctoral Candidate, Doctoral Program in Computer Science

Department of  Computer Science

Poltak Sihombing, Universitas Sumatera Utara

Professor, Doctor of Computer Science

Department of Computer Science

Amalia Amalia, Universitas Sumatera Utara

Doctor, Head of Computer Science Study Program

Department of Computer Science

Marischa Elveny, Universitas Sumatera Utara

Doctor, Lecturer – Master's Program in Data Science and AI

Department of Computer Science

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Development of a hybrid information retrieval–knowledge graph model for cross-framework competency alignment

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Published

2026-04-30

How to Cite

Maulana, H., Sihombing, P., Amalia, A., & Elveny, M. (2026). Development of a hybrid information retrieval–knowledge graph model for cross-framework competency alignment. Eastern-European Journal of Enterprise Technologies, 2(2 (140), 32–42. https://doi.org/10.15587/1729-4061.2026.358313

Issue

Vol. 2 No. 2 (140) (2026): Information technology. Industry control systems

Section

Information technology

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Copyright (c) 2026 Halim Maulana, Poltak Sihombing, Amalia Amalia, Marischa Elveny

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