Knowledge Management Ontologies in Software Engineering: Applications, Challenges and Future Research Directions - A Systematic Literature Review
Authors
Department of Computer Science, University of Uyo, Uyo (Nigeria)
Department of Computer Science, University of Uyo, Uyo (Nigeria)
Article Information
DOI: 10.51244/IJRSI.2026.1306000362
Subject Category: Computer Science
Volume/Issue: 13/6 | Page No: 4882-4902
Publication Timeline
Submitted: 2026-06-22
Accepted: 2026-06-28
Published: 2026-07-10
Abstract
Knowledge management has become increasingly important in software engineering due to the knowledge intensive nature of software development processes. However, traditional knowledge management approaches often face challenges related to knowledge representation, retrieval, sharing, and reuse. Ontologies have emerged as a promising solution by providing formal and semantic representations of knowledge that facilitate interoperability, knowledge integration, and intelligent decision support. This study presents a systematic literature review of knowledge management ontologies in software engineering, with the aim of synthesizing existing research on their applications, benefits, challenges, and future development opportunities. Relevant studies were identified through a structured search of major scientific databases and analyzed using predefined inclusion and exclusion criteria. The findings reveal that knowledge management ontologies have been applied across various software engineering activities, including requirements engineering, software design and development, software testing, software maintenance, project management, and software process improvement. Reported benefits include enhanced knowledge sharing, improved knowledge reuse, better decision making, increased software quality, and strengthened organizational learning. Despite these advantages, challenges such as ontology development complexity, knowledge acquisition difficulties, scalability limitations, integration issues, and user adoption barriers continue to hinder broader implementation. The review further identifies emerging research directions involving artificial intelligence, knowledge graphs, semantic interoperability, and intelligent software engineering environments. In conclusion, the study highlights the growing significance of ontology based knowledge management in software engineering and provides a comprehensive synthesis of current knowledge while outlining opportunities for future research and industrial adoption.
Keywords
Knowledge Management; Ontologies; Software Engineering
Downloads
References
1. Alavi, M., and Leidner, D. E. (2001). Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues. MIS Quarterly, 25(1), 107–136. https://doi.org/10.2307/3250961 [Google Scholar] [Crossref]
2. Alrumaih, H., Mirza, A., and Alsalamah, H. (2020). Domain Ontology for Requirements Classification in Requirements Engineering Context. IEEE Access, 8, 89899-89908. doi:https://doi.org/10.1109/ACCESS.2020.2993838 [Google Scholar] [Crossref]
3. Ammirato, S., Linzalone, R., and Felicetti, A. M. (2021). Knowledge Management in Pandemics. A Critical Literature Review. Knowledge Management Research and Practice, 19 (4), 415–426. [Google Scholar] [Crossref]
4. Aurum, A., Daneshgar, F., and Ward, J. (2008). Investigating Knowledge Management Practices in Software Development Organizations. Information and Software Technology, 50(6), 511–533. https://doi.org/10.1016/j.infsof.2007.05.005 [Google Scholar] [Crossref]
5. Bjørnson, F. O., & Dingsøyr, T. (2008). Knowledge Management in Software Engineering: A Systematic Review of Studied Concepts, Findings and Research Methods used. Information and Software Technology, 50(11), 1055–1068. https://doi.org/10.1016/j.infsof.2008.03.006 [Google Scholar] [Crossref]
6. Chaccour, C., Saad, W., Debbah, M., Han, Z., and Poor, H. V. (2024). Less Data, More Knowledge: Building Next Generation Semantic Communication Networks. IEEE Communications Surveys & Tutorials. doi:https://doi.org/10.1109/COMST.2024.3412852 [Google Scholar] [Crossref]
7. De Nicola, A., and Villani, M. L. (2021). Smart City Ontologies and Their Applications: A Systematic Literature Review. Sustainability, 13(10). doi:https://doi.org/10.3390/su13105578 [Google Scholar] [Crossref]
8. Dingsøyr, T., & Conradi, R. (2002). A Survey of Case Studies of the Use of Knowledge Management In Software Engineering. International Journal of Software Engineering and Knowledge Engineering, 12(4), 391–414. https://doi.org/10.1142/S0218194002001024 [Google Scholar] [Crossref]
9. Euzenat, J., and Shvaiko, P. (2024). Ontology Matching (3rd ed.). Springer. https://doi.org/10.1007/978-3-031-60694-2 [Google Scholar] [Crossref]
10. Farghaly, K., Soman, R. K., and Zhou, S. A. (2023). The Evolution Of Ontology In AEC: A Two-Decade Synthesis, Application Domains, and Future Directions. Journal of Industrial Information Integration, 36. doi:https://doi.org/10.1016/j.jii.2023.100519 [Google Scholar] [Crossref]
11. Fauzan, R., Hamidi, M, Z., Safitri, W. A., Siahaan, D. O., and Karimi, M. I. (2025). Ontology in Requirements Software Development Method: A Systematic Literature Review. Journal of Information Technology and Cyber Security, 3(1), 14-32. https://doi.org/10.30996/jitcs.12297 [Google Scholar] [Crossref]
12. Felderer, M., Méndez Fernández, D., Travassos, G. H., Kalinowski, M., and Sarro, F. (2023). Artificial Intelligence and Data-Driven Software Engineering: Research and Practice. Journal of Systems and Software, 198, 111588. https://doi.org/10.1016/j.jss.2022.111588 [Google Scholar] [Crossref]
13. Fraga, A. L., Vegetti, M. and Leone, H. P. (2020). Ontology-Based Solutions for Interoperability among Product Lifecycle Management Systems: A Systematic Literature Review. Journal of Industrial Information Integration, 20. doi:https://doi.org/10.1016/j.jii.2020.100176 [Google Scholar] [Crossref]
14. Fu, C., Jiang, H., and Chen, X. (2022). Modeling of an Enterprise Knowledge Management System Based on Artificial Intelligence. Knowledge Management Research & Practice, 1–13. doi:https://doi.org/10.1080/14778238.2020.1854632 [Google Scholar] [Crossref]
15. Gómez Pérez, A., Fernández López, M., and Corcho, O. (2004). Ontological Engineering. London, United Kingdom: Springer. https://doi.org/10.1007/b106454 [Google Scholar] [Crossref]
16. Guizzardi, G. and Guarino, N. (2024). Explanation, Semantics, and Ontology. Data & Knowledge Engineering, 153. doi:https://doi.org/10.1016/j.datak.2024.102325 [Google Scholar] [Crossref]
17. Happel, H. J. and Seedorf, S. (2006). Applications of Ontologies In Software Engineering. In Proceedings of the International Workshop on Semantic Web Enabled Software Engineering (SWESE 2006) (pp. 1–14). [Google Scholar] [Crossref]
18. Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., de Melo, G., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., Ngonga Ngomo, A. C., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., and Zimmermann, A. (2021). Knowledge Graphs. ACM Computing Surveys, 54(4), 1–37. https://doi.org/10.1145/3447772 [Google Scholar] [Crossref]
19. Husáková, M., and Bureš, V. (2020). Formal Ontologies in Information Systems Development: A Systematic Review. Information, 11, 66: 1 – 18. doi:https://doi.org/10.3390/info11020066 [Google Scholar] [Crossref]
20. Idrees H., XU, J., Haider, S., Tehseen S. (2023). A Systematic Review of Knowledge Management and New Product Development Projects: Trends, Issues and Challenges. Journal of Innovation and Knowledge, 8, 100350, 1 – 10. [Google Scholar] [Crossref]
21. Kitchenham, B., and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. EBSE Technical Report EBSE-2007-01, Keele University and Durham University. [Google Scholar] [Crossref]
22. McGuinness, D. L., & van Harmelen, F. (2004). OWL Web Ontology Language Overview. World Wide Web Consortium (W3C) Recommendation. https://www.w3.org/TR/owl-features/ [Google Scholar] [Crossref]
23. Musen, M. A. (2015). The Protégé Project: A Look Back and a Look Forward. AI Matters, 1(4), 4–12. https://doi.org/10.1145/2757001.2757003 [Google Scholar] [Crossref]
24. Noy, N. F. and McGuinness, D. L. (2001). Ontology Development 101: A Guide to Creating your First Ontology. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05. [Google Scholar] [Crossref]
25. Olan, F., Arakpogun, E. O., Suklan, J., Nakpodia, F., Damij, N., and Jayawickrama, U. (2022). Artificial Intelligence and Knowledge Sharing: Contributing Factors to Organizational Performance. Journal of Business Research, 145, 605-615. doi:https://doi.org/10.1016/j.jbusres.2022.03.008 [Google Scholar] [Crossref]
26. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71 [Google Scholar] [Crossref]
27. Pan, J. Z., Vetere, G., Gomez-Perez, J. M., & Wu, H. (Eds.). (2022). Exploiting Linked Data and Knowledge Graphs in Large Organizations. Springer. https://doi.org/10.1007/978-3-030-82085-1 [Google Scholar] [Crossref]
28. Petersen, K., Vakkalanka, S., & Kuzniarz, L. (2015). Guidelines for Conducting Systematic Mapping Studies in Software Engineering: An Update. Information and Software Technology, 64, 1–18. https://doi.org/10.1016/j.infsof.2015.03.007 [Google Scholar] [Crossref]
29. Pliatsios, A., Kotis, K., and Goumopoulos, C. (2023). A Systematic Review on Semantic Interoperability in the IoE-enabled Smart Cities. Internet of Things, 22. doi:https://doi.org/10.1016/j.iot.2023.100754 [Google Scholar] [Crossref]
30. Rus, I. and Lindvall, M. (2002). Knowledge Management in Software Engineering. IEEE Software, 19(3), 26–38. https://doi.org/10.1109/MS.2002.1003450 [Google Scholar] [Crossref]
31. Said, A., Zhao, Y., Derr, T., Shabbir, M., Abbas, W., and Koutsoukos, X. (2023). A Survey of Graph Unlearning. doi:https://doi.org/10.48550/arXiv.2310.02164 [Google Scholar] [Crossref]
32. Studer, R., Benjamins, V. R., and Fensel, D. (1998). Knowledge Engineering: Principles and Methods. Data and Knowledge Engineering, 25(1-2), 161–197. https://doi.org/10.1016/S0169-023X(97)00056-6 [Google Scholar] [Crossref]
33. Tamburis, O., Esposito, C., and Taticchi, C. (2023). Artificial Intelligence and Ontology Engineering: Opportunities and Challenges for Semantic Knowledge Systems. Information Systems Frontiers, 25(5), 1821–1838. [Google Scholar] [Crossref]
34. Tudorache, T. (2020). Ontology Engineering: Current State, Challenges, and Future Directions. Semantic Web, 11(1), 125-138. doi:https://doi.org/10.3233/SW-190382 [Google Scholar] [Crossref]
35. Wongthongtham, P., Chang, E., Dillon, T., and Hussain, F. (2009). Ontology Based Knowledge Management Systems in Software Engineering. Journal of Universal Computer Science, 15(15), 2906–2933. [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
- Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
- Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
- Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
- Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet