The topic of value alignment in AI has been gaining significant attention. The ultimate objective is how AI systems can align with human values. One of the main challenges, however, is identifying the relevant values. Some emerging works are focusing on learning relevant values through analysing our statements on social media. In most of these cases, learned values are simply specified through a label such as equality, fairness, or justice. However, the semantics of these values is not studied further. Addressing this gap, we propose a user study that provides us with a systematic approach for learning value semantics. The study is in the context of healthcare and the values of interest are the four fundamental bioethical principles: beneficence, non-maleficence, autonomy, and justice. We conduct a user study to collect the views of medical professionals on the value alignment of synthetic patient cases. We then search the space of candidate functions to find the one that best fits the participants answers, and hence, best describes their view of the value in question.