During the analysis of a document, all nodes
in the taxonomy tree that are addressed by the text-analysis
process are highlighted, and the ensemble of highlighted nodes indicates
the thematic areas covered by the document.
The corresponding thematic areas of each document are then projected into a 100-dimensional content-space, and finally, a categorization of the documents is achieved by means of a self-organizing neural network (Kohonen-Map), ending up with the documents grouped in "well-organized bookshelves." The neural network also provides a scientifically founded similarity measure based on information-theoretical principals that allow the comparison of documents according to their content.
This content recognition and categorization technology works
across several different languages, recognizing, for example, that an
English translation of a French, German, Italian, or Spanish document
has the same content and contains the same information as the original
Unlike other systems, categorization with InfoCodex functions automatically, without any user intervention. This function eliminates the cumbersome and costly training for documentation classification - a significant advantage.