Search engines rely on the terms plugged in by users to determine which results to put through their algorithms, order and then return to the user. Yet, rather than simply recognizing and retrieving exact matches for query terms, search engines use knowledge of semantics (the science of the language) to construct intelligent matches for queries. An example might be a search for avo cigars that also returned results that did not contain that specific phrase, but instead had the term avo smokes.

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 bulk that is the Internet has given search engines some of the most accurate data about connections between words ever assembled artificially. This immense knowledge of word ontologies and their usage gives them the ability to determine what the topic of a page or site is, which pages in a site are topically related, how the link structure of the web divides into topical communties and more.

Search engines’ growing intelligence about the subject of language means that queries will increasingly return more evolved, intelligent results. This heavy investment in the field of natural language processing will help to achieve greater understanding of the intent and meaning behind their users’ searches. 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.  Good stuff!

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