The ability to accurately assess the type and extent of knowledge a user possesses without having to demand that it is explicitly declared is of importance for a range of different applications. To obtain this ability, the authors have developed an approach that harnesses the benefits of granular computing in order to apply granular and hierarchical structure to knowledge. In this way, identification of different knowledge domains and degrees of granularity becomes possible that is essential when assessing a user's knowledge. Furthermore, a method is described that could be used to improve the identification of knowledge by not relying exclusively on what a user has contributed. Content that is related to previous contributions and matches certain criteria can also be included in a bid to obtain accurate and richer predications of what knowledge a user possesses.