Enhancing non-expert understanding of Artificial Intelligence solutions through an ontology-based approach
To drive AI understanding and adoption, the vast terms, concepts, and definitions around the many different dimensions and aspects of AI must be demystified for a broader non-tech-focused audience. Such as professionals with higher education who work in a digitalized business environment but have no education in computer science or similar fields. These in this context, called “non-experts” of AI, who are decision-makers and contributors in various industries, could profit from an easy, manageable, and trustworthy way to level up their own knowledge of the AI field. Specifically in the context of relevant business challenges and terms.
Castratori, Sarah Graziella, 2024
Art der Arbeit Master Thesis
Auftraggebende
Betreuende Dozierende Laurenzi, Emanuele
Keywords
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In the awareness phase, the needs of non-AI-expert business stakeholders are gathered through interviews. The identified relevant frameworks from academia and the needs of the interviewees set the base for the suggestion phase. Here, the conceptual development of a solution design is created that focuses on the non-expert's needs. First, a taxonomy is built based on business terms and user requirements, setting the foundation for an ontology creation. The concept is then translated into a machine-readable form in the development phase. This sets the foundation for evaluating the approach, which is done with user-derived competency questions queried through SPARQL as well as feedback from the interview participants.
Studiengang: Business Information Systems (Master)
Vertraulichkeit: öffentlich