Ontology-Based Knowledge Integration Framework: A Systematic Review

Said M.E. Sidi F. Ishak I. Jusoh Y.Y. Jabar M.A. Abdullah L.N. Sharif K.Y. Gerasimova Y. Moldakhmetov S. Aitymova A. Bazarbayeva A.
January 2025 Insight Society

International Journal on Advanced Science, Engineering and Information Technology
2025 #15 Issue 6 1973 - 1979 pp.

The key to the competitive edge of knowledge-based firms is Knowledge Integration (KI) for improved efficiency, effectiveness, and innovation. Despite its significance, KI faces substantial technical challenges, primarily due to the variety of knowledge representations that impede integration. To address these problems, Knowledge Integration Frameworks (KIFs) have been developed as structured systems or models that support integration, prevent duplication, and inform decision-making. This paper presents an overview of ontology-based KIFs for the last five years. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we identified 47 articles in Scopus and Web of Science (WoS). Consequently, we selected 18 high-quality studies based on strict inclusion/exclusion criteria. The survey discusses the construction and employment of ontologies within the knowledge inference frameworks. Although ontologies can provide precise semantics, existing platforms primarily rely on structured data within specific domains, which limits their applicability to unstructured or cross-domain contexts. Furthermore, a majority of them are stakeholder-excluded, unscalable, and lack uniform evaluation benchmarks. Most of the work is based on case studies with little consideration of quantitative benchmarks. Notably, 22% of the identified methods support partial automation in KI, 17% provide methods for stakeholder feedback, but none assess performance measures or comparisons. These findings suggest a disjunction between theoretical constructs and applied principles. This study contributes to the field by presenting limitations and future directions. There is a desire for designs applicable across multiple substitutable disciplines, benchmark datasets, and stakeholder-informed methods. IJASEIT is licensed under a Creative Commons Attribution-Share Alike 4.0 International License

Knowledge Integration (KI) , Knowledge Integration Frameworks (KIFs) , Ontology

Text of the article Перейти на текст статьи

Department of Computer Science, Faculty of Computer Science and Information Technology, University Putra Malaysia, Malaysia
Department of Software Engineering and Information System, Faculty of Computer Science and Information Technology, University Putra Malaysia, Malaysia
School of Investigative Science, Enforcement Leadership and Management University (ELMU), Malaysia
Department of Multimedia, Faculty of Computer Science and Information Technology, University Putra Malaysia, Malaysia
Department of Computer and Information Science, Faculty of Science and Information Technology, Universiti Teknologi PETRONAS, Malaysia
Department of Energetic and Radioelectronics, Faculty of Engineering and Digital Technology, Manash Kozybayev North Kazakhstan University, Kazakhstan
Department of Theory and Methods of Primary and Preschool Education, Faculty of Pedagogical, Manash Kozybayev North Kazakhstan University, Kazakhstan
Department of Computer Science, Faculty of Information Technologies, L. N. Gumilyov Eurasian National University, Kazakhstan

Department of Computer Science
Department of Software Engineering and Information System
School of Investigative Science
Department of Multimedia
Department of Computer and Information Science
Department of Energetic and Radioelectronics
Department of Theory and Methods of Primary and Preschool Education
Department of Computer Science

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026