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.

Geometric objects such as polygons, line segments, and points may have manifold relations among each other, i.e., order, adjacency, connectivity, etc., and may be stored in a database. For the design of the spatial data structure and in order to preserve consistency when manipulating the data, we propose a graph grammar approach. All consistent states are described by a structure graph, and the manipulation rules are given by productions where intersection problems as well as topologic properties have to be solved. By appropriately modeling the behavior of geographic data, consistency is preserved at all times. This eliminates the tedious case of recovering a geographic database after an inconsistency has been detected.