Our knowledge liquidization and crystallization framework addresses this issue.
The idea is very simple. Knowledge liquidization decomposes a knowledge
representation into atomic units. Knowledge crystallization identifies new
relationships among some of the units capturing an emerging context. That is, in
contrast with knowledge generalization and instantiation, knowledge liquidization
divides a cohesive structure into coherent units while knowledge crystallization
discovers a new cohesive structure among coherent units. In order to drive this
discovery process, the framework demands the involvement of human interaction in
the process [Hori 1994][Nakakoji et al. 1998].
The next section presents our approach of the use of computational support for
the framework.