The first semantic map was generated at the end of 2018 by processing Wikipedia. After that, we began making semantic maps for exploratory search, thematic analysis, and domain visualization. To demonstrate the capabilities of the technology, Semantic map for Wikipedia was released in mid-2019.
The results left no one indifferent: we had a vivid picture that is the embodiment of the entire human knowledge. And this picture can be explored infinitely. Each area of the map has its own meaning. You can fathom the knowledge contained in the map by exploring the neighboring regions and see how the keywords gradually change, passing into other areas. You can encounter quite unexpected topics and trace how they are connected.
Beyond Wikipedia, we have processed medical articles, papers on economics, and legal documents. Semantic maps have made it possible to discover very quickly which documents have utmost importance, while revealing relationships between topics, which are sometimes intuitively clear and sometimes unexpected.
The Semantic Map is a powerful tool to analyze collections of texts. It can be created for any collection of documents in any language. The Semantic Map is the cornerstone of the Silk Data Semantic Framework.