As a result, self-describing ontologies can become a reality, which in turn can be used for risk management, knowledge building, and verification, team planning, collaborative learning, monitoring, and even for development of physical entities like advanced materials construction or for scientific advancement in promising new technology markets like biochemistry. In fact, automatic and dynamic emulation modeling becomes a tremendously powerful byproduct.

Inductive associations
This concept of inductive association is key to exploring related content that can be more meaningful than the discovery context itself. Inductive association helps uncover the "killer detail" that I described in previous articles, where one doesn't know about influential behavior often until it becomes too late.

Using the concept of data reflexivity described above, the data can become "your eyes under the hood," watching relevant details as they form to minimize surprises, maximize opportunities and to notice emerging problems ahead of the competition. Using inductive association to bring user learning urgency curves upfront, corporations can have their users prepare relevant knowledge not just-in-time, but ahead-of-time, since user knowledge inculcation necessarily requires a gestation period to facilitate intuitive decision making.

This, in turn, is key to risk management. See my article on managing corporate IQ as it relates to corporate and user plasticity at http://www.dominopower.com/issues/issue200107/corporateiq001.html.

Self-generated iconographic cognitive collaborative maps
Furthermore, tools like XSLT (Extensible Stylesheet Language Transformations) to auto-generate browser-side XML, Javascript, and DOM (Document Object Management), browser-side and intelligent device parsers can be automatically instructed to read generated knowledge objects and views, displaying their recursive associations in a cone-tree metaphor for users to incrementally explore context-driven knowledge.

Selected icons representing data in the current cognitive mapping view or additional user assertions, questions, or representative documents can be added to the mix and sent to the Knowledge Server via a SOAP (Simple Object Access Protocol) XML knowledge transaction. There will be more details on this below.

The icons can be recontextualized to hunt relevant associated knowledge, which in turn would be brought back to the client and displayed in the browser as a descriptive cognitive map in an incremental learning fashion.

Such a self-simplifying, self-describing, and self-organizing visual representation of underlying knowledge associations provides a powerful incremental learning facility. User navigational paths can be captured as representative of their learning patterns and the knowledge server can use this to leverage the user's personal learning style and context.

When data relevance is influenced with the user's experiential context and learning patterns, as well as the requisite discovery context, user learning is a much richer, deeper, motivational experience, providing a natural motivational phenomenon required to manage corporate aliteracy.