Content recognition and categorization


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 document.

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.

Content similarity
Semantic and similarity search
Abstracts Generation
Advanced visualization
Privacy and security
Semantic Web
Customer benefits
The semantic engine
Distributed Sources
Cross-lingual text analysis
Content recognition and categorization