Decisions suggested by improperly designed software systems might be prone to discriminate against people based on protected characteristics, such as gender and ethnicity. Previous studies attribute such undesired behavior to flaws in algorithmic design or biased data. However, these studies ignore that discrimination is often the result of a lack of well-specified fairness requirements and their verification. The fact that experts' knowledge about fairness is often implicit makes the task of specifying precise and verifiable fairness requirements difficult. In related domains, such as security engineering, knowledge graphs have been proven to be effective in formalizing knowledge to assist requirements specification and verification. To address the lack of formal mechanisms for specifying and verifying fairness requirements, we propose the development of a knowledge graph-based framework for fairness. In this paper, we discuss the challenges, research questions, and a road map towards addressing the research questions.