While content management is key in this process, it represents only part of the technology required to build successful knowledge management applications. Learning management technology is essential for leveraging content repositories and for building dynamical self-propagating knowledge bases. By folding usage behavior and collaborative learning pattern associations back into corporate knowledge repositories, self-propagating information "churn" can become sustainable.

Information churn represents the dynamic change in knowledge associations that emanate from the infusion of new information into a knowledge base or from information associations generated by knowledge content turnover through usage. Studies in knowledge portal usage show that portals fall into disuse where information churn is not designed into the application.

Sustaining information churn is a significant and costly effort when it isn't automated. Therefore, it makes sense to automate information churn through self-propagating systems that feed on themselves for maximizing knowledge information value-added. Automated learning management technology, when added to content management systems, can provide the self-propagating churn necessary to sustain usage on an affordable basis.

Churning beyond search into learning management
Search and retrieval is only the first stage in the knowledge management life cycle. To leverage information content for knowledge management, it must be simplified into knowledge context through a process of automatic semantic normalization. Auto-classification is one tool that can assist in content normalization. Other tools, like auto-summarization, auto user profiling, and auto collaboration profiling, represent other methods for normalizing content into knowledge context. Each semantic view represents a dimensional perspective of the underlying knowledge context.

Collaborative cognitive mapping interfaces represent an iconographic learning tree metaphor that permits users to not only view, navigate, and hunt intuitively for meaningful knowledge associations in underlying knowledge repositories, but also to capture their navigational pathways and fold them back into the knowledge base to enhance associative relationships they visited.

This ability to fold multiple semantic view and collaborative navigation associations back into knowledge repositories adds to the automatic energy formation of information churn, thereby facilitating sustainable usage of the underlying knowledge repository.

Adding to churn with deep semantic associations
Information content can also be automatically componentized using advanced methods like fractal semantic indexing. Knowledge index servers that use fractal semantic indexing can create repositories that are self-organizing, self-simplifying, and self-describing--that are dynamic and organic.

The process of automatically and continually componentizing and reorganizing information content along multidimensional optimization lines can add to the propagated energy formation of information churn, especially where information usage is automatically granularized down to the atomic component level.