Using a modal dialog box or even a tool tip, paragraph numbers listed with their documents in support of respective themes can be preloaded so that you can explore the content in a responsive manner. You can do this by simply holding the mouse over the paragraph number and visually scanning the resulting text snippets as explanations of the dimensional semantics pivoting around respective taxonomy themes are made visible.

Documents can provide multiple views showing a table of contents as a rendition of its knowledge signature, a short document summary as a gist of what the document is about, a long summary semantically tagged with various rules, and profiles and dictionaries. You can also get kSig themes that lift out visually pertinent knowledge from the text for productive learning experiences, where each paragraph reflects text that is semantically tagged for themes represented, with a list of themes appended as a paragraph header highlighting the concepts described within.

Automatic multidimensional semantic slicing-and-dicing for knowledge discovery
Using the kThread of the results vector, the same kThread that produced the results taxonomy can be re-ordered by strength of results to create a three-axis pivot as a three dimensional Knowledge Comparator in order to facilitate the learning process. This creates an associative semantic triplet necessary for automating multidimensional hypercube binding. In this view, content can be componentized around results vector themes, showing unique text at each theme, representing associated document paragraphs with a formal reference to the document and paragraph from which it came for published works referencing.

Displaying unique content is key so as not to repeat content in describing themes along results vectors, facilitating a productive incremental learning process through semantic triplets, which in turn deliver the fractal compression required in multidimensional semantic hyperspace to facilitate automatic pattern formation.

Other views, such as "More Detail on Theme" and "Related Detail to Theme," can be threaded off the results kThread to provide additional semantic discovery and comparator axis. The More Detail Themes view provides the context for all document paragraphs associated with the theme being explored. The Related Detail To Theme view provides the first unique occurrence of document paragraph context associated with themes that are on the same kSig in the Results kThread but not on the kThread itself. This provides not only an incremental learning experience, but also an inductive learning experience so that users can explore content that is associated indirectly with kThread themes.

Fractal semantic patterns form automatically through semantic convergence, much like genetic algorithms, providing the opportunity to derive semantic schemas rendered in XML (eXtensible Markup Language) derivatives like kSigs, kThreads, and baysean-type outcome trees for inductive and deductive inferencing. Fractal pattern formation also facilitates the requisite entropy to drive self-simplifying, self-organizing, and self-describing knowledge bases.