International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 16, No. 4, 8 Aug. 2026
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Ontological Cores of Documents: A Construction Methodology and Use Cases
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Author(s)
Index Terms
Digitized Document, Semantic Model, Structural Ontology, Core Ontology, Ontological Core, Knowledge Engineering, Digital libraries, Semantic Web
Abstract
The preservation of rare documents in the form of image collections presents significant challenges regarding access to their documentary content. To enable this accessibility for software agents, this article proposes a formal representation of this type of document through a semantic description layer. This layer includes a set of descriptive metadata attached to the document, alongside the minimal and strictly necessary vocabulary required to formalize the explicit textual and visual knowledge of its documentary content. To achieve this, we present a construction methodology based on a Semantic Model of Document (SMD), where a document is treated as a core documentary resource containing a set of information resources. The semantic description of these resources, aligned with RDF framework logic, produces an Ontological Core of Document (OCD) that formally describes the document's logical structure and captures its underlying semantics. Finally, we demonstrate the practical utility of these Ontological Cores through three distinct use cases—each targeting a specific dataset level (structural, administrative, and semantic)—showing how they allow software applications to move beyond simple collection searching toward intelligent, precise information extraction directly from the documentary content.
Cite This Paper
Mohammed El Ouaazizi, "Ontological Cores of Documents: A Construction Methodology and Use Cases", International Journal of Education and Management Engineering (IJEME), Vol.16, No.4, pp. 1-15, 2026. DOI:10.5815/ijeme.2026.04.01
Reference
[1]N. Cheriet, “L’Impact du Numérique sur la Transmission du Patrimoine Culturel : Entre Opportunités et Défis,” Zaouli, vol. 9, no. 1, pp. 367–414, 2025, Available: https://www.revue-zaouli.com/, Accessed: Jun. 10, 2026.
[2]L. Asprino, A. G. Nuzzolese, and V. Presutti, “Semantic Web and Knowledge Graphs for Cultural Heritage: Systematic Review and Future Directions,” Semantic Web, vol. 15, no. 2, pp. 189–212, 2024, doi:10.3233/SW-230508
[3]A. Patel and S. Jain, “Present and Future of Semantic Web Technologies: A Research Statement,” International Journal of Computers and Applications, vol. 43, no. 5, pp. 413–422, 2021, doi:10.1080/1206212X.2019.1601625
[4]A. K. Ibrahim, “Evolution of the Web: From Web 1.0 to 4.0,” Qubahan Academic Journal, vol. 1, no. 3, pp. 20–28, 2021, doi:10.48161/qaj.v1n3a54
[5]S. A. Salloum and M. Al-Emran, “Information Retrieval Systems in the Era of Knowledge Graphs and Semantic Search: Challenges and Opportunities,” Journal of Information Science, vol. 51, no. 1, pp. 78–95, 2025, doi:10.1177/01655515231174987
[6]A. Latif, A. Rasheed, U. Sajid, et al., “Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review,” Mathematical Problems in Engineering, vol. 2019, Art. no. 9658350, 2019, doi:10.1155/2019/9658350
[7]A. M. Mithun and Z. A. Bakar, “Empowering Information Retrieval in Semantic Web,” International Journal of Computer Network and Information Security, vol. 12, no. 2, pp. 41–48, 2020, doi:10.5815/ijcnis.2020.02.05
[8]J. Li, A. Sun, J. Han, et al., “A Survey on Deep Learning for Named Entity Recognition,” IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 1, pp. 50–70, 2022, doi:10.1109/TKDE.2020.2981314
[9]E. M. K. El-Awadly, A. I. Ebada, and A. M. Al-Zoghby, “Arabic Handwritten Text Recognition Systems and Challenges and Opportunities,” The Egyptian Journal of Language Engineering, vol. 10, no. 2, pp. 84–103, 2023, doi:10.21608/ejle.2023.195029.1042
[10]R. Gartner, “Extracting Semantics from Document Layouts: Linking Structural Analysis to RDF Datasets,” Journal of Documentation, vol. 81, no. 2, pp. 290–311, 2025, doi:10.1108/JD-01-2024-0012
[11]J. Jagtap and N. Bhosle, “A Comprehensive Survey on the Reduction of the Semantic Gap in Content-Based Image Retrieval,” International Journal of Applied Pattern Recognition, vol. 6, no. 3, pp. 254–271, 2021, doi:10.1504/IJAPR.2021.118556
[12]A. M. Blair, Too Much to Know: Managing Scholarly Information before the Modern Age, New Haven, CT: Yale University Press, 2010.
[13]P. Hitzler, “Semantic Document Models for Knowledge Representation and Interoperability,” Computers in Industry, vol. 154, Art. no. 104031, 2024, doi:10.1016/j.compind.2023.104031
[14]T. Tudorache, “Ontology Engineering: Current State, Challenges, and Future Directions,” Semantic Web, vol. 11, no. 1, pp. 125–138, 2020, doi:10.3233/SW-190381
[15]F. A. Arakaki, R. C. V. Alves, and P. L. V. A. da Costa, “Dublin Core: State of Art (1995 to 2015),” Informação & Sociedade, vol. 28, no. 2, pp. 165–178, 2018, doi:10.22478/ufpb.1809-4783.2018v28n2.34005
[16]F. Brinis, M. O. Soualah, and F. Bouarab, “Comparative Study of Encoding Formats for Digitized Ancient Arabic Manuscripts,” in Proceedings of the 24th International Arab Conference on Information Technology (ACIT), 2023, pp. 1–11, doi:10.1109/ACIT58888.2023.10444312
[17]F. Neuhaus and J. Hastings, “Ontology Development Is Consensus Creation, Not (Merely) Representation,” Applied Ontology, vol. 17, no. 4, pp. 495–513, 2022, doi:10.3233/AO-220275
[18]L. Elmhadhbi, M. H. Karray, and B. Archimède, “Aligning Upper-Level Ontologies with Domain-Specific Vocabularies: A Framework for Semantic Interoperability,” Computers in Industry, vol. 158, Art. no. 104092, 2024, doi:10.1016/j.compind.2024.104092
[19]E. Aminu, I. Oyefolahan, M. Abdullahi, and M. Salaudeen, “A Review on Ontology Development Methodologies for Developing Ontological Knowledge Representation Systems for Various Domains,” International Journal of Information Engineering and Electronic Business, vol. 12, no. 2, pp. 28–39, 2020, doi:10.5815/ijieeb.2020.02.04
[20]N. Aussenac-Gilles, “Donner du Sens à des Documents Semi-Structurés : De la Construction d'Ontologies à l'Annotation Sémantique,” Séminaire INRIA, ADBS, vol. 5, pp. 125–150, 2012, Available: hal.science, Accessed: Jun. 10, 2026.
[21]A. Bikakis, E. Hyvönen, S. Jean, et al., “Semantic Web and Linked Data Technologies for Cultural Heritage: Current Trends and Future Perspectives,” Semantic Web, vol. 15, no. 1, pp. 1–12, 2024, doi:10.3233/SW-230541
[22]G. Alemu and E. Garoufallou, “The Future of Interlinked, Interoperable, and Scalable Metadata in Open Data Environments,” International Journal of Metadata, Semantics and Ontologies, vol. 17, no. 1, pp. 54–68, 2024, doi:10.1504/IJMSO.2024.100620
[23]R. T. Fielding, R. N. Taylor, J. R. Erenkrantz, et al., “Reflections on the REST Architectural Style and 'Principled Design of the Modern Web Architecture',” in Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, 2017, pp. 4–14, doi:10.1145/3106237.3117765
[24]E. Hyvönen, et al., “Cultural Heritage Knowledge Graphs on the Semantic Web: Current Trends and Future Perspectives,” ACM Journal on Computing and Cultural Heritage, vol. 17, no. 2, pp. 1–22, 2024, doi:10.1145/3631114
[25]A. Rossi and T. Clérice, “From Pixels to Graphs: Interlinking HTR Transcriptions with Semantic Web Ontologies for Historical Manuscripts,” International Journal on Digital Libraries, vol. 26, no. 1, pp. 45–63, 2025, doi:10.1007/s00799-024-00401-4
[26]N. Carboni, et al., “Modular Ontology Engineering for Heterogeneous Cultural Heritage Collections,” Semantic Web, vol. 15, no. 4, pp. 589–612, 2024, doi:10.3233/SW-230536
[27]L. Zhang and M. Schilling, “Evaluating Reasoning Efficiency in Localized vs. Monolithic Knowledge Graphs for Digitized Archives,” Journal of Web Semantics, vol. 84, Art. no. 100812, 2025, doi:10.1016/j.websem.2024.100812