Content similarity

Heat map showing the "hot spots"

The neural network provides a well founded similarity measure based on information-theoretical principles that allow the comparison of documents according to their content.

The proximity of the documents implies a high level of similarity (and vice versa). In mathematical terms, the similarity measure is given by the weighted scalar product of the two vectors, corrected by the Kullback-Leibler distance from the main themes, combined with the weighted score sum of the matching keywords and their nodes in the taxonomy tree, respectively.

The patented similarity measure is independent of the document language and is only somewhat dependent on the exact wording. It allows InfoCodex to recognize document families, i.e. documents very similar in content to each other, but that do not necessarily share the same keywords or expressions. This is also the fundamental process for automatic generation of abstracts.

The similarity measure is a solid basis for

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
Content similarity