Semantic search uses XML and RDF data from semantic networks to disambiguate search queries and Web text (Wikipedia). How does Google use it to improve the SERP?
Googlebot is able to rank sites that stem the search term (derivates of a word), but it goes further, and also searches on synonyms for words that have multiple meanings or that return few matches. Therefore ranking is not exclusively based on exact matches. It is also able to suggest closely related terms when the exact matches aren’t found.
Personalised search helps disambiguation, cause search engine supposes the fixed meaning by the information it has about you.
What about anchor text in links? Is Google improving regarding anchor text keywords and keyphrase?
Through methods of anaphora resolution from computational linguistics it is possible to obtain more occurrences of a word in a text. Traditional keyword density calculations will be marginally less important.
It’s been suggested the possibility that Google builts a world wide word density cloud (a very comprehensive dictionary) and use that to abstract the depth of information for a search term.
Semantic search allows boots to read the intent of the page as well as the content.
