These multiple high-level subject classification hierarchies represent dynamic taxonomies that can be user, interest group, or business task oriented. This permits you to drill into the core of detailed aggregations in and across hierarchies to the source of the data for information discovery from multiple perspectives.

Adaptive classification taxonomies dynamically adapt, growing incrementally and experientially to represent the reality of the underlying domain of data, so that there are no surprises from hidden data that's forced to fit the closest classification node. Each document's classification hierarchy stands on its own, and is added to the interrelated value of the domain's inter-networked classification trees, creating a rich knowledge fabric.

Adaptive classification taxonomies aren't limited to one time or periodic snapshots generated from a representative sample of underlying data, which can quickly fall out-of-date in a global market changing at a rapid pace. They maintain pace with new and altered documents, one document at a time.

Adaptive classification taxonomies can dynamically track your interest, knowledge, and collaborative profiles. They can publish interest profiles as taxonomies of what you read, knowledge profiles of what you write, and collaborative profiles of nodes written and read showing the transfer of knowledge from writer to reader.

Document signatures for new documents can be matched against documents in classified databases to inherit the classification that sufficiently and best fits its signature.

You can be notified automatically when new documents are created or updated, or when document collections reach an aggregate point of significance that sufficiently fits your interest profile. Managers can also be notified when collaborative profile patterns reach an aggregate point of significance.

Your knowledge growth can be tracked to manage and facilitate the development of your organization's intellectual capital, helping the corporation manage to the business plan.

Adaptive classification hierarchies provide a powerful data mining environment, permitting you to discover the interconnections from any node of interest and drill through the details to the core source material using the underlying interconnected, hyperlinked document classification trees. This provides a directed learning environment facilitating knowledge discovery.

Since you don't have to switch context to another discovery tool, you can maintain focus without disruption. This way you can complete your discovery process through the hyperlinked knowledge media of the interconnected document classification trees.

Expert user navigation, discovery routes, and query paths can be logged, aggregated, and published dynamically as taxonomies using XML, based on the combined significance of path usage. This provides yet another taxonomy view, which dynamically builds usage class taxonomies as multidimensional viewers of the same document domain, creating an extended classification of multidimensional taxonomies themselves. The usage class taxonomy facilitates the capturing, monitoring, and managing of the best practices and respective user concurrence dynamically.