An approach for generalising symbolic knowledge

Many researchers and developers of knowledge based systems (KBS) have been incorporating the notion of context. However, they generally treat context as a static entity, neglecting many connectionists' work in learning hidden and dynamic contexts, which aids generalization. This paper presents a method that models hidden context within a symbolic domain achieving a level of generalisation. Results indicate that the method can learn the information that experts have difficulty providing by generalising the captured knowledge. © 2008 Springer Berlin Heidelberg.