What is semantic search?
Semantic search aims to uncover user intent. In contrast, lexical search uncovers exact iterations of the words and phrases that are being searched for.
Using a semantic search, search engines can uncover the most relevant content for users, while reducing the occurrence of spam and low-quality content on the SERP. For example, searching for “learning to sew” a few decades ago would result in content that included that exact phrase multiple times on the page. The content may or may not teach the reader how to sew.
Today, when searching “learning to sew,” quality articles, likely with illustrations, will show up on the SERP, even if the exact phrase wasn’t used in the content. That’s because search engines utilize algorithms that can deduce the meaning of a piece of content and relate it to the phrase that was searched for.
In the case of “learning to sew,” a search engine may also look for content that mentions making clothing, fixing clothing, working with fabric, and a wide range of other related words and phrases. Content with many of these related terms on the page will rank higher than content that is deemed less relevant.
Why search engines prefer semantic search over lexical search
One of the biggest benefits of semantic search over lexical search is the fact that search engines can cut down on spam and low-quality content. With a lexical search, content with the keyword or phrase used the most would occur first on the page. That encouraged content producers to keyword stuff content in order to ensure their pages showed up first on the SERP.
When the use of certain keywords and phrases is most important, quality often suffers. Search engines have solved this problem with semantic search. Although algorithms still use keywords and phrases to give the search engine an idea of what’s on the page, related terms, images, and other page details are equally important. This cuts back on the amount of keyword stuffing that is done on a page, and it encourages content producers to create high-quality content.
This type of search also enables algorithms to understand search queries among the masses. For example, when searching for a superstar like Oprah on the Internet, the search engine will return pictures of Oprah Winfrey, movies she has appeared in, and her personal social media accounts instead of retrieving information with the word “Oprah”, which would have created a less relevant list of results.
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SEO and semantic search
The use of semantic search by search engines has changed the way people create content for the Internet. Keyword stuffing is no longer the norm, as it is now punished by Google. Instead, it’s all about providing value to readers.
First, content creators should think carefully about what they want to be known for. Once that question is answered, the goal should be to become the authority on that topic. Dive deeply, provide readers with real value, and Google will rank the content highly.
How the content is written is important, too. Sentences should be clearly structured without using language that is too elementary or complex. Flesch-Kincaid Readability Tests are a great way to make sure content scores well before it is posted on the Internet.
There is still a place for keywords and phrases with semantic search. Choose a single keyword or phrase with a density of no more than two-percent in the content. Consider brainstorming related keywords and phrases that can be used in the content, or allow them to occur naturally.
Structured data is a great way to ensure bots can identify the topic being discussed. That means creating an easy-to-use navigation bar, backlinking to relevant interior web pages, and posting relevant, consistent content across multiple platforms on the Internet. It gives a semantic search platform relevant content that is easy to access, enabling the search engine to more accurately determine the topic of content on the site.