Search engines rely on the terms queried by users to determine which results to put through their algorithms, order and return to the user. But, rather than simply recognizing and retrieving exact matches for query terms, search engines use their knowledge of semantics (the science of language) to construct intelligent matching for queries. An example might be a search for loan providers that also returned results that did not contain that specific phrase, but instead had the term lenders.
The engines collect data based on the frequency of use of terms and the co-occurrence of words and phrases throughout the web. If certain terms or phrases are often found together on pages or sites, search engines can construct intelligent theories about their relationships. Mining semantic data through the incredible corpus that is the Internet has given search engines some of the most accurate data about word ontologies and the connections between words ever assembled artificially. This immense knowledge of language and its usage gives them the ability to determine which pages in a site are topically related, what the topic of a page or site is, how the link structure of the web divides into topical communities and much, much more.
Search engines’ growing artificial intelligence on the subject of language means that queries will increasingly return more intelligent, evolved results. This heavy investment in the field of natural language processing (NLP) will help to achieve greater understanding of the meaning and intent behind their users’ queries. Over the long term, users can expect the results of this work to produce increased relevancy in the SERPs (Search Engine Results Pages) and more accurate guesses from the engines as to the intent of a user’s queries.