Automating multidimensional knowledge assimilation
When we recombine document signatures from multiple perspectives by using different documents as directional knowledge queries, we get an effect similar to OLAP (On-Line Analytical Processing) as an orderly, multidimensional knowledge segmentation of knowledge views in and across a document collection. I call this eKAP (electronic Knowledge Assimilation Processing).

When knowledge is ordered in multidimensional, hyperlinked eKAP extracts, users can "slice and dice" the knowledge in multiple compound views to uncover new meaningful relationships. By collaborating with the user's own experiential knowledge in this manner, adding supportive comments, details, and ideas into the eKAP knowledge extract, not only can the knowledge base be grown, but the extract can also be used to generate self-directed knowledge queries into internal and external knowledge sources. This will strengthen knowledge relationships that are weak within the extract itself and even within the knowledge base proper.

As a result of all this, simply by working your knowledge to discover context relationships and responding to knowledge queries, the corporate knowledge base grows in a dynamically adaptive incremental fashion. It shapes itself by adjusting continually to new information added during the knowledge discovery and fulfillment process by its users in true Darwinian fashion. The knowledge base not only converges on relevant knowledge relationships automatically, but it also reflects, with incremental clarity through goodness-of-form and goodness-of-fit, the type of knowledge required and applied to the business. This permits corporate resources to be highly tuned and synchronized to respond in a timely manner as business knowledge needs occur.

Context switching is unproductive
Knowledge Management is really about bringing the context of answers into the user's experiential context. That way users can grow their understanding from that with which they are familiar. This permits users to quickly segment and interpret what they need from source material and to ignore whatever isn't relevant. Forcing users to switch context to get answers significantly reduces the experiential context upon which the user can build associations to derive meaningful answers.

When the gap between the need and the answer is too big, the learning experience breaks down and all information is disassociated into meaningless InfoGlut. This results in a negative learning experience and brings about few returns. However, when the gap is within the user's semantic reach, called their "Semantic Horizon," they have sufficient experiential context for the new material. This results in a stronger and larger common context upon which to learn new material.

The brain's neuro-chemical transmitter, dopamine, burns in the synaptic pathways of the organ when the new associations from the learning are formed. This results in a feel-good euphoria, similar to a "runner's high." We call this phenomenon the "learning high." This encourages the user to search for more new learning experiences in order to repeat the feeling.