Enhancing OntoUML Accessibility through Structured Text Generation and Text-to-Speech

OntoUML is a modeling language for creating conceptual ontologies, which often presents challenges in comprehension for non-experts. Additionally, its graphical representation poses accessibility challenges for visually impaired individuals. This research addresses these issues by exploring methods to enhance the understanding and accessibility of OntoUML models through structured text generation and text-to-speech conversion. I propose a translation approach that converts OntoUML models into controlled natural language, using Semantics for Business Vocabulary and Business Rules (SBVR) as an intermediate representation, and subsequently into speech. This approach aims to make OntoUML models more accessible and improve their usability, thereby; facilitating collaboration between modelers and other stakeholders, including individuals with visual impairments.