This review surveys information uncertainty modelling approaches in domain ontologies, highlighting trends and gaps.
Domain ontologies are essential for representing and reasoning about knowledge, yet addressing information uncertainty within them remains challenging. This review surveys approaches to modelling information uncertainty in domain ontologies from 2010 to 2024. It categorizes modelling formalisms, identifies information uncertainty types, and analyzes how information uncertainty is integrated into ontology components. It reviews reasoning techniques and emerging methods, including Machine Learning and Natural Language Processing. The review examines languages, tools, and evaluation strategies. The purpose is to map the landscape of information uncertainty modelling in domain ontologies, highlight research gaps and trends, and provide structured guidance for selecting suitable approaches.
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Alomair et al. (2026) studied this question.
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