The comprehension of similarity metrics lags behind that of distance metrics. This study aims to address this disparity by synthesizing the properties of similarity metrics and examining them through the lens of weighted epistemic logic. By incorporating these metrics, we analyze knowledge systems in terms of their metric properties. Modal logic techniques, including bisimulation and bounded morphism, are employed to investigate the definable and undefinable properties of similarity. Definable alternatives for undefinable properties are proposed.