Work place: Laboratory of Engineering Sciences (LSI), Polydisciplinary Faculty of Taza, USMBA University, Morocco
E-mail: mohammed.elouaazizi@usmba.ac.ma
Website: https://orcid.org/0009-0005-5013-2225
Research Interests:
Biography
Mohammed El Ouaazizi is an Assistant Professor at the Polydisciplinary Faculty of Taza, Morocco. He received his Ph.D. in Computer Science from Sidi Mohamed Ben Abdellah University (Fez, Morocco) in 2015, with a dissertation focused on ontologies and the semantic description of digitized documents. Prior to his academic career, he acquired over twenty years of industrial experience within various electronics and IT corporations. His current teaching and research interests include computer architecture, operating systems, XML engineering, semantic Web technologies, and ontology engineering. His recent research focuses on leveraging knowledge-based systems and ontologies to build intelligent applications for e-learning, digital libraries, and open data.
DOI: https://doi.org/10.5815/ijeme.2026.04.01, Pub. Date: 8 Aug. 2026
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.
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