KAOS is one of the most widely used Goal-Oriented Requirements Engineering (GORE) methods. The goal model is the central element of KAOS, employed to represent the goals of a system in the form of a hierarchy, where higher-level goals are refined into lower-level ones. The process of constructing a KAOS goal model for a new application can present challenges, requiring significant time and effort. Existing approaches have tried to partially automate the construction of goal models, however, this largely remains a complex, manual task. In this paper, we propose leveraging domain knowledge in the form of a Knowledge Graph (KG), which can assist the application designer in creating goals that are inspired from this knowledge. To accomplish this, we leverage semantic similarity measurement and Natural Language Inference (NLI) to effectively extract triples from the KG that are relevant to a high-level goal formulated by the designer. The extracted triples are further processed through sentiment analysis and graph-to-text generation, before presented to the designer. Via step-by-step interaction with our solution, the designer can gradually refine their initial goals into a goal hierarchy. We perform an empirical evaluation of our approach by applying it to the design of a flood management system, based on a handcrafted domain KG.