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What Is Semantic Search? How It Impacts SEO

Semantic-Search

Have you ever been amazed at the ability of Google to handle almost any query you throw at it?

Let’s take this for an example:

google search ex

We asked Google’s “whose that iron suit guy” and despite not mentioning Iron Man by the name, the search giant understood what we are talking about.

This incredible ability would not have been possible without semantic search.

If you are dabbling with search engine optimisation, you have to know what is semantic search and how it affects SEO. In this post you are going to learn everything about semantic search, why it is important and how to adapt your SEO for semantic search.

What is semantic search?

Semantic search is a new age information retrieval process that is used by all modern search engines to get the most relevant search results for the users. Instead of traditional keyword matching, it focuses more on meaning behind search queries.

The terminology “semantic search” comes from the branch of linguistics called semantics, that studies the meaning of words and phrases.

Why is semantic search important?

We don’t have to tell you, how important it is for search engines to return relevant search results for the users. There are countless variables at play here, but the principles of semantic search remain important because of the following reasons:

  • Users often use a different language than the content that they desire
  • Many search queries are unintentionally ambiguous
  • There is a need to reflect personal interests and trends in search queries
  • Search engines have to understand lexical hierarchy and entity relationships

We are sure that at some point in time even you might have difficulty in articulating a search query properly. The most common example is when you hear an unfamiliar song on the radio and start Googling randomly with the lyrics until you find the song.

Also, try to co-relate how you search into Google with what you say to your Google Assistant or Siri or Alexa. When you are interacting with the smart devices, the keywords become conversations. There are so many ways to express the same idea and to give relevant results search engines need to deal with all of them. An efficient search engine is the one that can match the content in their index with the meaning of your search query.

Now you might be getting the idea how challenging this can be. But this is just the beginning.

Many searches are unintentionally ambiguous

Did you know that 40% of English words are polysemous, that is, they have 2 or more meanings. This adds a significant challenge and semantic search is trying to solve this issue.

Let’s take the keyword “python” as an example. This keyword has over 533000 monthly searches in the United States alone.

Now, if you are in the industry and you are searching for this keyword you are most probably referring to the programming language. But anyone outside the tech industry might be actually searching for actual snake or the legendary British comedy troupe.

The problem for search engines is that words alone do not have a definite meaning without context. And when you add polysemous words then things get a lot more complicated. You can anticipate a finding literal meaning is so hard how difficult it will be to delve into inferred meaning like sarcasm.

In semantics, context is everything. This brings us to most important relationships-lexical hierarchy and entity relationships.

Lexical hierarchy and entity relationships

Take a look at this example.

complex_query_example

It is truly impressive, how Google is able to understand the meaning of this query.

It is understood that “partner” means wife/girlfriend/husband/boyfriend/spouse.

Understood which actors played Obi-Wan in multiple movies and series

Made the connections

Displayed search results in a way that reflects the ambiguity of “obi wan.”

Whether you believe it or not, you would have never got the same result if you searched in 2010 or earlier.

So how exactly Google is able to pull this off.

Lexical hierarchy illustrates the relationship between words. In this example, the word partner is superordinate to wife, girlfriend, spouse and others.

As you can see, our queries often don’t match the exact wording of the desired content. This is why search engines have to know lexical hierarchy of the keywords. In our example, Obi-Wan is associated with people with a specific job (actor) and then associating these people with their partners.

We hope you understand the complexity here because we cannot go further into language intricacies as it will easily go beyond the scope of the post.

Personal interests and trends reflection

Once again let’s go to the python example. When we search for this keyword we get results related to the programming language.

Now this is true for us, but not for you. No matter how much we dislike the fact that our personal data is been used, it is at least useful for search engines to make our search queries more personal. Google uses limited data together with our search history to deliver more personalised and accurate search results.

For example, if you search for a service in Google, you will get localised results:

What is even more fascinating is Google’s ability to temporarily at just the search results based on dynamically changing search intent.

The most relevant example at present is the coronavirus. It is not a new term and has always been known for a group of viruses. But as we know, at the beginning of 2020, the search intent changed rapidly. People started looking for information for a particular strain of coronavirus (SARS-CoV‑2), and search results had to be adjusted accordingly.

serp-history-graph-coronavirus

As you can see in the graph above, the SERP position history for “coronavirus” none of the top 5 search results ranked before 2020.

This is also a common event in the e-commerce industry during sales events like Christmas and Black Friday. The search intent during those days become highly transactional as compared to people or ordinarily search.

Google Technologies And Semantic Search

Google is known for continuously pushing out algorithm updates and technologies capable of better understanding natural language and search intent.

Where the semantic search today is, is the result of 4 important milestones:

  1. Knowledge Graph
  2. Hummingbird
  3. RankBrain
  4. BERT

Google’s Knowledge Graph, first released in 2012, is simply a knowledgebase of entities and the relationships between them. The simplest example of this is represented by following image, but in respect of Google expand this to over 5 billion entities.

knowledge-graph

In simple words, it was a technology that start it and enabled the shift from keyword matching search to semantic matching. The main 2 methods that empowered knowledge graph are structured data and entity extraction from text.

Hummingbird was a Google algorithm update that was released back in 2013. It was the first colossal update, that enabled the search giant to emphasise more on the meaning of search queries over individual keywords. It took the SEO industry by storm as poorly written content with keywords got completely obligated from the search results.

If you have heard of Latent Semantic Indexing or LSI keywords, then you are already in the mix of Google’s LSI solving algorithm the RankBrain.

Google’s RankBrain is powered by technologies that are much more superior than LSI. In simple words, RankBrain can understand the meaning of even unfamiliar words and phrases by using sophisticated machine learning algorithms. This is a huge achievement for Google considering the fact that 15% of all search queries are new.

BERT or Bidirectional Encoder Representations from Transformers is the most recent and a huge upgrade to how semantic search works. It affected approximately 10% of all search queries by the end of 2019. There is a lot that goes into this upgrade but all you need to know about BERT is that it helps the search engine to understand long and complex sentences enquiries. It is a solution to deal with ambiguity and nuisances because it can understand the context of words better. While there is nothing per se that you can do to optimise for this upgrade, creating relevant content is what you should focus on.

How to adapt your SEO for semantic search

By now you have got a good witness to any of semantic search and it’s important for the future. It’s time to learn some truly actionable information to align your SEO with semantic search.

  1. Target topics, not keywords

Long gone are the days when you can rank by targeting individual keywords and using them in your content. In the old days, you could have easily ranked for slightly different keywords using separate pieces of content for the same topic.

However, those days are gone. Google now understands what searches mean to the point that it can infer the meaning of slightly different keywords and ranks mostly the same pages for all of them. Now when you are creating content, keep this in mind and do not try to create a new piece of content for every keyword. Instead, focus on covering a topic in-depth by using lot of similar and longtail keywords.

  1. Understand search intent

Sometimes you can create the best content for cover a certain topic without aligning with the search intent.

For example, you are covering the topic on SEO report creation. Naturally you will be covering everything needed to create the best SEO report. Suppose you came up with something like, “Using QUERY Function to Create the Best SEO Report.” Here you might create the best piece of content that ultimately leads to the best is your report, but for most people the QUERY Function might not be a familiar one.

This is why you need to look at the top-ranking pages before covering a new topic to understand search intent.

So, before you start outlining a new piece of content, look at the top-ranking pages to infer the search intent.

  1. Use semantic HTML

Semantic Search is a long process and you can start with semantic web. Previously, the whole concept of WWW was standardised interlinked documents without any explicit meaning. But now we need meaning to be relevant.

It all starts with basic HTML. Here is a comparison of 2 structures of HTML elements:

semantic HTML

With semantic HTML you can add meaning to the court so that machines can recognise navigation blocks, headers, footers, videos or tables. In HTML5 you will find most semantic elements that are already used in modern websites. If your website is still lacking semantic HTML, you better start upgrading it.

  1. Schema markup

Schema markup is an additional way of marking up the webpages. Also referred to as structured data it helps search engines to better understand webpages.

Schema.org vocabulary is where you will find hundreds of types that are associated with properties. When you add schema markup to your pages you are making it easier for Google to understand the content without complex algorithms.

  1. Build relevancy and authority through links

Links are one of the earliest indicators of relevancy. And in 2021, the quality of backlinks plays important role in ranking a website. Using both internal and external links helps Google to figure out the context of the pages-even before processing it.

Conclusion

Semantic Search has truly changed the whole content ecosystem. It is enabling search engines to deliver more relevant and valuable content to the users and motivating publishers to produce good quality content.

While at the goal there are sophisticated technologies and algorithms involved, but the principles of semantic search are very easy to understand. We hope this post will help you make any necessary changes to your SEO strategy to be future proof.

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