If you’re an SEO specialist, you’ll know that Google is slowly moving away from being a search engine to becoming an answer engine. Back in 2007, if you entered a query like “best restaurants in Dothan”, Google would have returned a set of search results that had these words in the title of the page. But, today, Google returns a direct list of top restaurants in Dothan along with information related to reviews, operation hours and prices. (Read this article about how to optimize your website)
Similarly, for a search query like “What is the height of eiffel tower?”, Google returns a direct answer to the question that is – “300 m”.
Google slowly moved away from processing a search query made up of “strings” to “things”. Today, Google is able to correctly identify the objects present in the search query and is able to identify the relations between the objects in order to compute the real intent of the user. All this happens because of semantic search.
Semantic search is the technology that transformed Google from a search engine to an answer engine.
What is Semantic Search?
Semantics is the study of meanings and in context to Google search, semantic search relates to a data searching technique that identifies the meaning of the words present in the search query in order to identify and present the best set of search results to the user.
Alexis Sanders gave an awesome explanation of semantic search in the form of Simpsons family. The figure helps us to understand what Google sees as entities and how Google identifies the attributes and the relationships between entities.
Hence, when you search on Google with the query “What is the name of Simpsons dog?”, Google returns a direct answer – “Laddie”.
When Did Semantic Search Came Into Existence?
The first semantic search invention was patented by Google in the year 1999 and it was entitled “Extracting patterns and relations from “scattered” databases such as the world wide web”.
If you are good with equations and numbers then you can read this patent that mentions the DIPRE method and the pattern relation duality.
How Does Google Processes Query Using Semantics?
In order to understand the processing of a search query using semantic search technology, please refer to the below steps:
Step1: A search query is entered by the user.
Step 2: The search query is received by the search engine.
Step 3: The search query is parsed using a parser. The parser is a computer program that is part of a compiler. It analyzes a string of words in a natural language. In simple terms, it breaks down the query into parts which can be further processed to determine the meaning of the query.
Step 4: The relationship between the search terms are identified using an association database described in the patent : system and method for providing search query refinements. The association database is also known by the name Knowledge Graph and the search results of various queries are directly fetched with the help of Knowledge Graph.
Step 5: It picks up documents that matches the intent of the user based on the identified entities and their relationships.
Step 6: The search results are displayed before the user in the form of a direct answer or (and) a set of webpages.
Please note that Google Hummingbird uses the power of both semantic search and knowledge graph to process search queries.
You can also have a look at the below figure to have an idea of Google query processing in a semantic environment:
How To Make Your Webpages Semantic Search Friendly?
As you have known until now that semantic search is all about understanding the intent of the user and presenting the best set of search results that directly answers the user’s query. Hence, in order to make your webpages semantic search friendly, you need to follow the below tips:
- Try to present the content in the form of Q&A pattern because it becomes easier for the search engines to interpret the content as direct answers. This helps the content to get ranked in the featured snippet as a direct answer to the question.
- Google uses a metric called TF-IDF score for the proper refinement of queries and to determine the relevancy of webpages. You will increase your chances of ranking higher in the search results if you are able to get your TF-IDF score right. Head over to this post to learn more about it and how you can use it for SEO.
- Add relevant images and videos in your content with respect to your target keyword. Always think of the user first and present the content in a manner that it becomes easier for the user to understand what you are trying to convey. If you think adding a video in your content will make it easier for the audiences to understand your content then take that extra effort in creating a video.
- Semantic search takes into account topics instead of keywords so try to narrow down the focus of your page’s content to a single topic per page.
- Take the help of org to accurately add metadata in your webpages. This will help Google and other search engines to better understand the contents of the page.
In essence, semantic search does not look at lexical matches of the search query, instead it looks at the overall meaning of the search query. In the years ahead, voice search is set to play a greater role in the identification of search intent and Google will return accurate search results in the form of voice instead of text based search results. In fact, it has already started doing so but it is still in its infancy.