Nassira Achich

Nassira Achich is a researcher at the Cédric Laboratory, Conservatoire National des Arts et Métiers (CNAM), specializing in temporal data representation and imperfection handling within Semantic Web frameworks. Her work develops OWL 2 ontological solutions for uncertain, imprecise, and conflicting temporal data with applications in Alzheimer's care systems and collective memory projects.

She holds a PhD jointly awarded by HESAM Université and Université de Sfax (Tunisia) in 2021 for her thesis on temporal data imperfection in Alzheimer's patient software. Her educational background bridges French and Tunisian academic institutions with a focus on practical healthcare applications.

Dr. Achich's research centers on integrating imperfection theories (possibility, probability, evidence theory) into semantic technologies for robust temporal reasoning. She addresses core challenges in representing uncertain time intervals, resolving data conflicts, and modeling imprecision within ontological frameworks, contributing significantly to knowledge representation under uncertainty.

Analysis of her 12 publications (2019-2024) reveals consistent methodological progression from foundational typologies of temporal imperfection (2019) to advanced ontology implementations (2024). Her work spans theoretical comparisons of uncertainty frameworks and practical implementations in healthcare contexts, published in venues including Journal of Intelligent and Fuzzy Systems, New Generation Computing, and ISWC.

No scientific awards or honors are documented in the source material.

Information regarding student supervision, grant funding, or administrative roles is absent from the provided publications list. Her academic contributions appear focused on independent research within the Cédric Laboratory's collaborative environment.

As a member of Cédric Laboratory's research teams, she contributes to axes including 'Complex data, machine learning and representations' and 'Data mining and statistics,' working within interdisciplinary groups advancing semantic technologies for real-world data challenges.