Natural Language Processing
NLP is a most fascinating branch of computer science. It is concerned with computers being able to understand natural or human language. Obviously, the better this can be accomplished, the better the results will be from applications such as search engines. The many challenges which still exist in this field limit the methods implemented by today's search engines.
NLP encompasses such topics as Latent Semantic Analysis (LSA), aspects of which are currently the most widely implemented efforts to understand natural language.
LSA is a technique in natural language processing which employs a vector space model to analyze semantic relationships between a set of documents and the terms they contain.
LSA can also use a co-occurrence matrix to determine which terms are rare, or statistically improbable phrases, and which terms are common phrases.
While mathematical analysis have proven useful, they clearly have their limitations. No matter how refined the analysis, the results will always have to rely on the corpus, or body of documents, used to determine the co-occurrence. This will always allow for error.
The Semantic Web holds the promise to change all that by offering a database of definitions from which applications can find the exact meaning and relationships for any specified term.
Krakken combines the idea of the aforementioned semantic relationships with information about the statistical usage of terms to determine not only which terms are meaningful for your market, but also the most appropriate location in your website blueprint for these terms.