Understanding The Language Of Users With Semantic Search For SEO


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Humans, by nature, are hard to understand as they evolve and change over time. That is one of the reasons why search engines like Google often conduct algorithm updates so they can continue to deliver relevant and accurate results to the users’ search intent. To further understand the meaning and context behind every search intent of users, Google improves its search formula by implementing semantic search, which gives it a better understanding of users’ language. This article shall discuss the definition of semantic search, give examples, and show the four major turning points that drive and improve search search approach: Knowledge Graph, Google’s Hummingbird, Google’s RankBrain, and Google’s BERT.

Life without the Internet is almost unimaginable because we do most of our work on the Internet. With the Internet, different parts of the world have become more connected.


Search engines like Google are stepping up their game to create more holistic search results to optimise users’ experiences. While this has been good for users, it can be challenging for SEO vendors.


Before, most, if not all, SEO vendors used tricks to optimise web pages that are only for search engines and not users. That is why search engines often update and improve other necessary processes to make the search results more accurate and user-friendly. 


Semantic search is one of these methods. Even though it can be tricky, we will explain its definition and show some examples. 


What Is Semantic Search?

Suppose a user searches for a string of words. Words and phrases could have a different meaning to different searchers depending on their search intent, especially given how vast the digital space is. As a result, the search results might bring answers for the user that are related to their search intent. 


That is when the semantic search comes in. Search engines like Google use semantic search to know what a user is looking or asking for online. Understanding the human language requires comprehending the search intent, query context, and relationship between terms entered into the search field..


In a holistic approach, here is how semantic search works. 


In this image, the user searches for ‘order a pizza’, and Google understands the search intent as the user that is looking for a pizza place where they can order nearby.


Example Of Semantic Search


To give another example, for the image below, the user searches for ‘make a pizza’, which explains why the search engine results pages (SERPs) are full of pizza recipes. 


Second Example Of Semantic Search


If the user only searches for ‘pizza’, the results they will get are most likely local SEO content. But the web pages shown on SERPs will change if the user’s search history is all about pizza recipes. Because of the personalisation components involved, you can expect the search results will display more about how to make a pizza rather than showing a list of pizza places. 


In other words, semantic search affects the web pages that will appear on SERPs, based on users’ intent and search history, which helps improve the accuracy of every search result.


4 Biggest Turning Points That Drive And Improve Semantic Search Approach

The internet has become something that most people nowadays cannot live without. It is hardly surprising given that everyone uses the internet for both personal and business purposes.


The problem, however, is that users’ search intent is adapting as it reflects life changes, the experience of everyone and everything involved. What people meant by a particular search intent today might have a different meaning and context in the future. 


But with semantic search, that is not a problem. It can understand the complexity of search intentions as they evolve continuously, along with the consumers’ behaviour and trends.  These were made possible by the four major turning points that are driving and improving the semantic search approach:


4 Biggest Turning Points That Drive And Improve Semantic Search Approach


1. 2012 – Knowledge Graph

The Knowledge Graph, also known as a semantic network, is the first turning point of semantic search. Google uses this knowledge-graph data model to understand entities and context better. The way it collected information has been helping the search engine provide more sensible answers to users.


The image shown below explains how Google’s Knowledge Graph looks like.


what googles knowledge graph looks like Example From Search Engine Journal

This Image came from Search Engine Journal


2. 2013 – Google’s Hummingbird

The following year after Google’s Knowledge Graph, Google’s Hummingbird was the milestone. By the time this algorithm upgrade was made public in September 2013, around 90% of queries made had been impacted worldwide.


Google’s Hummingbird uses natural language processing (NLP), which makes the search engine more intelligent. It was able to distinguish between search intents helping to give users better results depending on their searches.


They want to “get into a more ‘natural conversation’ between people and Google,” according to Google engineering director Scott Huffman, who spoke with Forbes Magazine.


In other words, Google’s Hummingbird was the reason why the search engine could understand and interpret the depth of meaning behind each word in the content. 


3. 2015 – Google’s RankBrain

Three years later, Google released another algorithm update that drove semantic search further. Given that RankBrain is based on machine learning technology that provides more relevant results to the users, it has become one of Google’s ranking factors, along with links and quality content. 


Google’s RankBrain is one of the key developments in semantic search since it can modify its algorithm and deliver more relevant results. This scenario is plausible given that RankBrain learns how to contrast a collection of terms it has never seen before with those it has. The derivatives of the comparison analysis done by RankBrain will help bring the most similar results. 


4. 2019 – Google’s BERT

The last and recent update that drives the development of semantic search is Google’s BERT (Bidirectional Encoder Representations from Transformers).


With the support of this algorithm update, users can obtain and find accurate information more efficiently Given that Google’s BERT helps the search engine focus on and understand conversational context and search intent from one language to another, the results on SERPs will become more accurate than ever before.

In Conclusion 

Now that you know how to create more relevant content based on Google’s semantic search, content creation will become easier because it is grounded on what the users’ are looking for. 


If you need help strategising for using SEO semantic search for SEO, reach out to an SEO agency like OOm. Both our SEO and content marketing team will help create more engaging and relevant content for your potential customers and appear more on SERPs. 


Contact OOm at +65 6690 4049 or leave a message on our website today to learn more about how we can help you take advantage of semantic search for SEO.