Ever since Google’s Hummingbird, the term “semantic search” has been thrown around a lot. Yet, the concept is often misunderstood. What is semantic search and how it helps SEO efforts?

When people speak to each other, they understand more than just words. They understand the context, non-verbal cues  (facial expressions, nuances of the voice, etc.) and so much more.

It comes naturally, so we don’t really appreciate how difficult it is to explain what is being communicated without the help of all “beyond-words” signals.

Factors that make the lives of both Google and SEO so difficult

  • Google is trying (and often struggling) to understand what it is that their users want (without actually seeing or hearing them)
  • SEOs are trying to reverse-engineer what it is that Google managed to understand from their users’ queries and how to build pages that meet those mysterious criteria. As Google’s algorithm is getting more mature, it is becoming even more difficult to decode what it is Google needs, or more importantly, what Google found its users need when using any specific search query.

This is where semantic search comes into play.

“Semantics” refers to the concepts or ideas conveyed by words, and semantic analysis is making any topic (or search query) easy for a machine to understand.

Put very simply (and I am not a professional semantic analysis expert, even though I do have a degree in cognitive linguistics), Google (and consequently SEOs) are dealing with two main concepts behind semantic search:

  • Semantic mapping, that is, exploring connections between any word/phrase and a set of related words or concepts.
  • Semantic coding, that is, using coding to better explain Google what types of information can be found on each page.

Because we tend to throw terms left and right in our industry (and often invent our own in the process), there’s lots of confusion when it comes to semantic search and how to go about it.

So this article is my attempt to clear that confusion and help you better understand semantic analysis and its application in SEO.

Semantic mapping

Semantic mapping is about visualizing relationships between concepts and entities (as well as relationships between related concepts and entities).

Here’s an example of a semantic map (or model) taken from a paper by Google’s Ramanathan Guha, the future creator of Schema project:

Semantic search modelImage Source

[Part of a semantic map for [Yo-Yo Ma] search query]

This model helps Google to better understand any of the related queries and provide helpful search cues (like knowledge graph, quick answers, and the others).

Image source: Screenshot created by the author (Dec 2019)

The semantic analysis also helps Google serve voice search users better by providing them with immediate answers based on their generic understanding of a topic.

But how can semantic analysis be used in search engine optimization?

The best way to understand semantics is offered by Text Optimizer, which is a tool that helps understand those relationships.

If someone is searching for [pizza], they may be looking to:

  • Order a pizza
  • Cook a pizza

From years of serving search results to users and analyzing their interactions with those search results, Google seems to know that the majority of people searching for [pizza] are interested in ordering pizza.

Example of understanding semantic searchImage source: Screenshot created by the author (Dec 2019)

Consequently, all we need to do is to decode Google’s understanding of any query which they had years to create and refine.

So Text Optimizer grabs those search results and clusters them in related topics and entities giving you a clear picture of how to optimize for search intent better.

Image Source: Screenshot created by the author (Dec 2019)

The idea is that using several of these terms in your copy helps put it right inside Google’s semantic model. This way Google knows that your document will do a good job matching the searcher’s intent.

Semantic coding

The idea behind using code to express meaning (not just presentation) goes years back, long before Schema.org project was launched.

For years, we’ve been using semantic HTML to communicate the meaning of content:

  • H1-H6 subheadings would map out the main topics of a document
  • Other HTML tags would express more semantics, including:

These tags help all kinds of machines to better understand and convey information they find on a web page.

For example, for accessibility, it is recommended to use the following markup for assistive technology to know where quotation starts (and ends) and who is cited.

Image Source: Screenshot created by the author (Dec 2019)

That’s how HTML tags add to the meaning of a document, and why we refer to them as semantic tags.

When Schema.org was created in 2011, website owners were offered even more ways to convey the meaning of a document (and its different parts) to a machine. From then on, we’ve been able to point a search crawler to the author of the page, type of content (article, FAQ, review, and other such pages) and its purpose (fact-check, contact details, and more).

So why would anyone care about semantic coding?

Semantic markup exists for one reason – The desire to communicate

We want to explain the purpose and the structure of our content to a search engine.

With the help of semantic markup, Google is able to identify and use key information from a page. In exchange, web publishers get “rich snippets“, that is, search listings that are more detailed than those that do not use semantics.

To help you with semantic coding, there are a lot of tools:

  • Schema App helps with just about any structured data markup that exists
  • For WordPress users, there are a variety of plugins created, including review schema, FAQ schema, and more.

Finally, the recent project called inLinks helps you add structured data to your pages based on their own semantic analysis.

Image Source: Screenshot created by the author (Dec 2019)

Conclusion

Put simply, semantic analysis is an attempt to bridge the gap between search algorithm, web pages it returns and the search engine’s users:

  1. A human being wants to find something and a search engine has two tasks to solve – Understand what the user wants and match that intent to web documents that do the best job meeting it
  2. A search engine needs to understand what it is they want to find. The semantic analysis is used to better understand the search query intent
  3. A search engine needs to match that query intent with web pages it has in the index. The semantic coding can be used to explain to a search engine what it is on the page and whether it matches the query intent.

As you can see the semantics is used to make the interactions between the search engine and its users easier, but it also helps the search engine to better understand (and use) the information on any page.

Ann Smarty is the blogger and community manager at Internet Marketing Ninjas. She can be found on twitter @seosmarty