What is Semantic Search?
Semantic search is an intelligent search process that aims to generate more accurate results by focusing on intent and the contextual meaning of keywords being searched for rather than simply looking for web pages that have been optimized for the keywords used in the search query. Google’s Hummingbird algorithm is powered by semantic search technology.
Pre-semantic search, SEO revolved around keywords and links. Effective SEO techniques consisted of creating low quality, keyword-rich articles and PBNs with thousands of artificial links pointing back to the target website using exact match anchor text.
Semantic search has shifted the focus from simply reading the keywords typed into the search query box to understanding the mindset of the searcher by analyzing their intent.
With semantic search, Google’s main objective was to generate more accurate search results, and bring a full stop to the manipulation of its search algorithms. It wanted to stop pages ranking simply because certain keywords appeared so many times on a particular web page and in backlinks going to that page.
The Focus on Searcher Intent
With semantic search, rather than attempting to match the individual keywords used in a search query, the search engine looks at the entire query as a whole and delivers search results that are based on the “searcher’s intent”, effectively moving from keyword-based searches to context-oriented searches. In simple terms, it considers the sentence in its entirety, understands the searcher’s intent and then looks for the most relevant pages to the search query.
Semantic search has done away with the inherent ambiguity of conventional search. For example, pre-semantic search, a search like “where can I buy black suede shoes in New York?” would return web pages that match each keyword in the query – buy, black, suede, shoes, New, York. It would also look for pages that have been optimized for the entire query.
By contrast, semantic search considers the entire sentence as a whole and tries to understand the searcher’s actual intent, rather than simply parsing through each of the keywords used in the search query.
Rather than looking for web pages optimized for the keywords typed into the search query box, the search engine looks for web pages that match the user’s intent. This means that optimizing a particular web page for specific keyword phrases has become less effective if the “theme” of the web page does not match the intent of the searcher.
In fact, for an increasing number of search queries, most of the top 10 search results on Google are not even optimized for the keywords used in the search query. This means Google’s algorithm is better able to find and return the “most relevant” web pages to a particular search query, even if a particular web page does not contain keywords used in the search query.
This is why it is still essential to reinforce the content you produce with relevant keywords, and keyword optimize the on-page elements of your web page accordingly.
However, the focus is now on pages that are deemed relevant based on the intent of the searcher rather than those that are best optimized for the keywords used in a search query.
The Knowledge Graph
The Knowledge Graph is a knowledge base used by Google to enhance the search results with information gathered from a wide variety of sources. It is designed to help Google provide more accurate and contextually relevant results by understanding the searcher’s intent and the real-life context of the search.
With the Knowledge Graph, Google interprets a search query as a search for an “entity” rather than a search for webpages that are best optimized for the keywords used in the search query.
Understanding the searcher’s intent behind asking the question and context such as location, previous searches and other information that Google already knows about the searcher, Google ensures the pages in search results match the meaning of the entire query and the intent of the searcher, rather than pages that match individual keywords.
The Knowledge Graph is based on the Hummingbird algorithm, and is the “brain” of semantic search. In other words, if you want to be discoverable in the search results of the future, semantic search engines need to be able to understand what entities are on your web page. Organic search on Google is now much more personalized, and the same search can yield different results to each searcher, based on their unique circumstances and intent.