Keywords
Semantic Web
ontologies
validation of educational models
skills gap
How to Cite
Abstract
The gap between the educational programs of higher education institutions (HEI) and the needs of the modern labor market is one of the key problems that negatively affects the employability of graduates. The mismatch between the skills that students receive in HEIs and the needs of employers complicates the employment process: graduates often have to gain additional skills outside the university, while it is more difficult for employers to find qualified specialists. One of the ways to solve this problem is the development of educational models using information and communication technologies (ICT) and their subsequent validation to identify gaps and improve curricula in accordance with the current needs of the labor market.
This paper presents an approach to the validation of educational models created using semantic technologies using the SHACL (Shapes Constraint Language) and SPARQL query language. SHACL enables checking the correctness of the data model by setting constraints on the structure and properties of the relationships between elements, such as disciplines and skills. It is an effective tool for imposing declarative constraints and validating the data structure. SPARQL, in turn, provides the ability to create flexible queries to RDF graphs, allowing you to create complex tests and analyze the relationships between model components.
To demonstrate the experiment, we presented a fragment of an educational model created using Semantic Web technologies. This model describes the relationships between academic disciplines and skills that are formed in students and the labor market requirements. The model was created based on a study of the Ukrainian labor market by analyzing data from job search platforms such as Work.ua, Robota.ua, and others. In this paper, we present a fragment of the educational model to demonstrate the capabilities of Semantic Web technology not only for building semantic models, but also, and above all, for their validation and verification.
References
C. Sarin, “Analyzing Skill Gap between Higher Education and Employability,” Research Journal of Humanities and Social Sciences, vol. 10, no. 3, p. 941, 2019, doi: https://doi.org/10.5958/2321-5828.2019.00154.2.
Coursera, “What Is the Skills Gap?,” Coursera, Feb. 19, 2024. [Електронний ресурс]. Доступно: https://www.coursera.org/enterprise/articles/what-is-skills-gap
S. Ruecker, “Encyclopedia of Multimedia Technology and Networking, Second Edition”. Rich-Prospect Browsing Interfaces. 2009. doi: 10.4018/978-1-60566-014-1.ch168.
M. Yarandi, H. Jahankhani, A. H. Tawil. “A personalized adaptive e-learning approach based on semantic web technology.” Webology 10. n. pag. 2013
W. Villegas-Ch and J. García-Ortiz, “Enhancing Learning Personalization in Educational Environments through Ontology-Based Knowledge Representation,” Computers, vol. 12, no. 10, p. 199, Oct. 2023, doi: https://doi.org/10.3390/computers12100199.
R. Hare and Y. Tang, “Ontology-driven Reinforcement Learning for Personalized Student Support,” arXiv (Cornell University), Jul. 2024, doi: https://doi.org/10.48550/arxiv.2407.10332
A. Agarwal, D. S. Mishra, and S. V. Kolekar, “Knowledge-based recommendation system using semantic web rules based on Learning styles for MOOCs,” Cogent Engineering, vol. 9, no. 1, Jan. 2022, doi: https://doi.org/10.1080/23311916.2021.2022568.
K. Rabahallah, L. Mahdaoui, and F. Azouaou, “MOOCs Recommender System using Ontology and Memory-based Collaborative Filtering,” Proceedings of the 20th International Conference on Enterprise Information Systems, 2018, doi: https://doi.org/10.5220/0006786006350641.
H. Gasmi and A. Bouras, “Ontology-Based Education/Industry Collaboration System,” IEEE Access, vol. 6, pp. 1362–1371, 2018, doi: https://doi.org/10.1109/access.2017.2778879.
Marek Milosz, Aizhan Nazyrova, Assel Mukanova, Gulmira Bekmanova, Dmitrii Kuzin, and Gaukhar Aimicheva, “Ontological approach for competency-based curriculum analysis,” Heliyon, vol. 10, no. 7, pp. e29046–e29046, Apr. 2024, doi: https://doi.org/10.1016/j.heliyon.2024.e29046.
Y. V. Rogushina, S. M. Pryima, “Ontological approach to qualification matching based on competences: model and methods,” Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. Jan. 2017.
“The ESCO ontology,” Europa.eu, 2025. [Електронний ресурс]. Доступно: https://ec.europa.eu/esco/lod/static/model.html. Дата звернення: 13 січня 2025.
G. Paquette, O. Marino, and R. Bejaoui, “A new competency ontology for learning environments personalization,” Smart Learning Environments, vol. 8, no. 1, Aug. 2021, doi: https://doi.org/10.1186/s40561-021-00160-z.
What are some best practices for validating and updating competency models? Linkedin.com, 2025. [Електронний ресурс]. Доступно: https://www.linkedin.com/advice/0/what-some-best-practices-validating-updating-1e. Дата звернення: 7 лютого 2025.
A. Spivakovsky, M. Poltoratskyi, O. Lemeshchuk, V. Denysenko, I. Karpov, and Y. Revenko, "Algorithms for using online tutoring as a tool for personalization of learning in an online educational environment," in Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2024, V. Ermolayev, et al., Eds., vol. 2359, Cham: Springer, 2025, doi: 10.1007/978-3-031-81372-6_13.
Preparation and presentation of data. Chatbot «Eva». 2024. [Електронний ресурс]. Доступно: http://about.ai.kspu.edu/#/7. Дата звернення: 14 січня 2025.
“Shapes Constraint Language (SHACL),” W3.org. [Електронний ресурс]. Доступно: https://www.w3.org/TR/shacl/. Дата звернення: 13 січня 2025.
“SPARQL 1.1 Query Language,” W3.org, 2013. [Електронний ресурс]. Доступно: https://www.w3.org/TR/sparql11-query/
“RDF 1.2 Turtle,” W3.org, Jan. 09, 2025. [Електронний ресурс]. Доступно: https://www.w3.org/TR/rdf12-turtle/ . Дата звернення: 13 січня 2025.
“SHACL Play!,” Sparna.fr, 2025. [Електронний ресурс]. Доступно: https://shacl-play.sparna.fr/play/ Дата звернення: 13 січня 2025.
“What is a ttl file?,” Oxfordsemantic.tech, 2024. [Електронний ресурс]. Доступно: https://www.oxfordsemantic.tech/faqs/what-is-a-ttl-file
“Notation3: A Practical Introduction,” Notation3.org, 2020. [Електронний ресурс]. Доступно: https://notation3.org/
RDF 1.2 TriG. RDF Dataset Language. [Електронний ресурс]. Доступно: https://www.w3.org/TR/rdf12-trig/
AtomGraph, “SPARQL Playground,” SPARQL Playground, 2025. [Електронний ресурс]. Доступно: https://atomgraph.github.io/SPARQL-Playground/. Дата звернення: 13 січня 2025.
REFERENCES (TRANSLATED AND TRANSLITERATED)
C. Sarin, “Analyzing Skill Gap between Higher Education and Employability,” Research Journal of Humanities and Social Sciences, vol. 10, no. 3, p. 941, 2019, doi: https://doi.org/10.5958/2321-5828.2019.00154.2. (in English)
Coursera, “What Is the Skills Gap?,” Coursera, Feb. 19, 2024. [Online]. Available: https://www.coursera.org/enterprise/articles/what-is-skills-gap (in English)
S. Ruecker, “Encyclopedia of Multimedia Technology and Networking, Second Edition”. Rich-Prospect Browsing Interfaces. 2009. doi: 10.4018/978-1-60566-014-1.ch168. (in English)
M. Yarandi, H. Jahankhani, A. H. Tawil. “A personalized adaptive e-learning approach based on semantic web technology.” Webology 10. n. pag. 2013 (in English)
W. Villegas-Ch and J. García-Ortiz, “Enhancing Learning Personalization in Educational Environments through Ontology-Based Knowledge Representation,” Computers, vol. 12, no. 10, p. 199, Oct. 2023, doi: https://doi.org/10.3390/computers12100199. (in English)
R. Hare and Y. Tang, “Ontology-driven Reinforcement Learning for Personalized Student Support,” arXiv (Cornell University), Jul. 2024, doi: https://doi.org/10.48550/arxiv.2407.10332. (in English)
A. Agarwal, D. S. Mishra, and S. V. Kolekar, “Knowledge-based recommendation system using semantic web rules based on Learning styles for MOOCs,” Cogent Engineering, vol. 9, no. 1, Jan. 2022, doi: https://doi.org/10.1080/23311916.2021.2022568. (in English)
K. Rabahallah, L. Mahdaoui, and F. Azouaou, “MOOCs Recommender System using Ontology and Memory-based Collaborative Filtering,” Proceedings of the 20th International Conference on Enterprise Information Systems, 2018, doi: https://doi.org/10.5220/0006786006350641. (in English)
H. Gasmi and A. Bouras, “Ontology-Based Education/Industry Collaboration System,” IEEE Access, vol. 6, pp. 1362–1371, 2018, doi: https://doi.org/10.1109/access.2017.2778879. (in English)
Marek Milosz, Aizhan Nazyrova, Assel Mukanova, Gulmira Bekmanova, Dmitrii Kuzin, and Gaukhar Aimicheva, “Ontological approach for competency-based curriculum analysis,” Heliyon, vol. 10, no. 7, pp. e29046–e29046, Apr. 2024, doi: https://doi.org/10.1016/j.heliyon.2024.e29046.
Y. V. Rogushina, S. M. Pryima, “Ontological approach to qualification matching based on competences: model and methods,” Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. Jan. 2017. (in English)
“The ESCO ontology,” Europa.eu, 2025. [Online]. Available: https://ec.europa.eu/esco/lod/static/model.html (accessed Jan. 13, 2025).
G. Paquette, O. Marino, and R. Bejaoui, “A new competency ontology for learning environments personalization,” Smart Learning Environments, vol. 8, no. 1, Aug. 2021, doi: https://doi.org/10.1186/s40561-021-00160-z.
What are some best practices for validating and updating competency models? Linkedin.com, 2025. [Online]. Available: https://www.linkedin.com/advice/0/what-some-best-practices-validating-updating-1e (accessed Feb. 07, 2025) (in English)
A. Spivakovsky, M. Poltoratskyi, O. Lemeshchuk, V. Denysenko, I. Karpov, and Y. Revenko, "Algorithms for using online tutoring as a tool for personalization of learning in an online educational environment," in Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2024, V. Ermolayev, et al., Eds., vol. 2359, Cham: Springer, 2025, doi: 10.1007/978-3-031-81372-6_13. (in English)
Preparation and presentation of data. Chatbot «Eva». 2024. [Online]. Available: http://about.ai.kspu.edu/#/7 (accessed Jan. 14, 2025) (in English)
“Shapes Constraint Language (SHACL),” W3.org. [Online]. Available: https://www.w3.org/TR/shacl/ (accessed Jan. 13, 2025) (in English)
“SPARQL 1.1 Query Language,” W3.org, 2013. [Online]. Available: https://www.w3.org/TR/sparql11-query/ (in English)
“RDF 1.2 Turtle,” W3.org, Jan. 09, 2025. [Online]. Available: https://www.w3.org/TR/rdf12-turtle/ (accessed Jan. 13, 2025) (in English)
“SHACL Play!,” Sparna.fr, 2025. [Online]. Available: https://shacl-play.sparna.fr/play/ (accessed Jan. 13, 2025).
“What is a ttl file?,” Oxfordsemantic.tech, 2024. [Online]. Available: https://www.oxfordsemantic.tech/faqs/what-is-a-ttl-file (in English)
“Notation3: A Practical Introduction,” Notation3.org, 2020. [Online]. Available: https://notation3.org/ (in English)
RDF 1.2 TriG. RDF Dataset Language. [Online]. Available: https://www.w3.org/TR/rdf12-trig/ (in English)
AtomGraph, “SPARQL Playground,” SPARQL Playground, 2025. [Online]. Available: https://atomgraph.github.io/SPARQL-Playground/ (accessed Jan. 13, 2025).
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright (c) 2025 Maksym Poltoratskyi, Olga Konnova