Understanding Semantic Search Delivers Better SEO

semantic search for seo

While Semantic Search has only recently become a reality in search engines, the idea of the “Semantic Web” has been around a bit longer. But, with recent advances in technology and algorithm changes, search engines have quickly evolved into next-generation answer machines that utilize both concepts. While many in the industry are still unclear as to what Semantic Search is and how it truly affects their SEO efforts, the reality is that Semantic Search has several components that can either help or hinder our future efforts. Since knowledge is power, education is always the first step to help you adapt to these changes. Let’s start off by first defining what Semantic Search is.

What is Semantic Search?

To understand what Semantic Search is, we must first understand what semantics means:  “the science of meaning in language.” It can also be interpreted to mean the context behind our language. So, Semantic Search, in essence means to understand the context of the language used in your search. While Semantic Search has just recently become an actuality, the “Semantic Web” as an idea was coined in 2001 by W3C Director Tim Berners-Lee who, coincidentally, invented the World Wide Web. Berners-Lee described the Semantic Web as a “group of methods and technologies to allow machines to understand the meaning, or “semantics” of information of the World Wide Web.” In simpler terms, Moz defines Semantic Search as “a search or a question or an action that produces meaningful results, even when the retrieved items contain none of the query terms, or the search involves no query text at all.” In simpler terms, the search engine extrapolates meaning from a search query, even without the use of keywords. Results have mainly been keyword driven and the concept of user intent and context without keyword inclusion seemed like science fiction. Until now.

What are Important Semantic Search Factors to Understand?

Fast-forwarding to 2014, we see that what was once a theory has now become reality because of search engine innovations. While Semantic Search sounds simple by definition – understanding context and user meaning in searches – in reality it is the sum of its parts. So, to better understand Semantic Search you must first take a closer look at important components like Google’s Knowledge Graph, Conversational Search, Structured Data and Semantic Keyword Research. A better understanding of Semantic Search and its components will help you know how to improve your ranking moving forward. Let’s start off by taking a look at Google’s Knowledge Graph.

What is Google’s Knowledge Graph and Why is it Important?

When someone performs a search, what are they looking for? Meta data, title tags and keywords? Of course not! They’re searching for an answer to their query or question. To better find these answers and not just regurgitate pages with proper keyword density, Google created a database or collection point for data named Knowledge Graph that was added to their search engine in 2012. Knowledge Graph pulls data from numerous web sources, like the CIA World Factbook, Freebase and Wikipedia, and delivers structured and detailed information about the search topic in addition to a list of links to other sites. Knowledge Graph contains more than 570 million objects, as well as more than 18 billion facts about and relationships between different objects that are used to understand the meaning of the keywords entered for the search. More simply put, Google’s Hummingbird algorithm is “accounting for every word in the query to get a better sense of context and the meaning behind the search, and it’s doing so not just from its Knowledge Graph but from essentially all web pages,” as stated by hothardware.com’s Seth Colaner.  This brings us to the next important factor to understand, conversational search.

What is Conversational Search, and How Does Semantic Search Make it Possible?

With incredible growth in mobile and tablet usage, and with better voice recognition software like Siri, Voice Search for Bing and Google’s Voice Search, it was only a matter of time before search engines would have to adapt their algorithms to better understand conversational language. In a study last year from Google, researchers determined that because it is more difficult to type on mobile devices and the keyboards on mobile devices aren’t as easy to use, users won’t spend much time searching for something specific. Instead, they would simply try some other means of getting the information. That’s where conversational search enters the equation. With Google’s recent Hummingbird algorithm update, better use of conversational search, or speaking your search query into the phone or search engine, is achieved because Google better understands relationships between words. This can be attributed to Semantic Search and Google’s Knowledge Graph. A spoken search is much different than a typed one.  Here’s an example: You might perform a voice search when you are driving and say “I’m hungry for a burger.”  Depending on where you are at that moment, the search engine will understand the correlation between hungry and restaurant and find a hamburger restaurant nearest to your location. Type the same search query into your home computer and you won’t get the same results, I promise. This is a good example of how Semantic Search in Google Hummingbird is helping make conversational search possible thanks to Google’s Knowledge Graph. That being said, where does our next important factor, structured data fit into the Semantic Search equation?

Does Structured Data Help Semantic Search?

Ever wish there was a way to help search engines better understand your website’s purpose, product price ranges, hours of operation, contact information, reviews and more, so the searcher can have more information right in their query and click on your site versus a competitor’s? That’s where Structured Data enters the picture. By adding structured data markup to the header of your website using the schema.org vocabulary and formats such as Microdata and RDF, alongside other approaches such as Microformats, you can help Google better understand your website. While it is not known if structured data is or will become a ranking factor for major search engines, Matthew Brown of Moz believes “Google and Bing will raise the bar on the quality of search results through the wider adoption of semantic data markup.” This means that a website’s use of this kind of markup will become increasingly important to help search engines better understand its key information so it can deliver more relevant search results. This means that structured data markup should become the rule not the exception. This leads us to my last important point: Are keywords dead?

Are Keywords Truly Dead Because of Semantic Search?

Let me pose this question: If you don’t have any content on your website, will a search engine be able to understand how to deliver it in search queries? Well no, not as well as if you did! While keywords are not as valuable as they once were in determining user intent and context because of Semantic Search, AJ Kohn, owner of Blind Five Year Old believes that “keywords let you create content that matches user intent.” So, without some sort of keyword strategy to help you write highly relevant and targeted content, search engines can’t successfully deliver your website in a search query because it won’t understand its context. While the old days of high keyword density are long dead, the use of targeted and diverse keywords to help craft quality content that answers user questions is very important because search engines still need to understand what a website is about before delivering search results.

Now that we know that keywords aren’t technically dead yet, I would like to pass along some useful keyword research tips that Sujan Patel of Search Engine Journal calls, “Semantic Keyword Research.” First, assemble a list of “Level 1” core keywords for your website. If you sold fans, for example, this could include industrial fans, commercial fans, etc. Second, create a list of thematically related “Level 2” keywords like, how to cool a shop, shop is hot, etc. Third, create keywords that answer user questions like, how to buy industrial fan, where to buy commercial fans, etc. Fourth, create quality website copy and outline future blog articles using the different keywords you discovered. Last, write for humans first, and search engines second. Always write quality articles that pass along valuable insight, so people will want to share them and Google will deem them relevant. The days of writing for search engines using keyword stuffing, hidden text and other black hat techniques no longer works in today’s world of Semantic Search.

Why Does This Matter?

In the ever changing world of SEO there is only one truth, that change is the norm. That’s why we love our job, it never gets boring! With Google or Bing always finding ways to keep us on our toes, adapting to these changes is what makes us better online marketers. Semantic Search, although a new reality, has been a theory for over 13 years, so adapting to that change will be inevitable, and when the next change comes along, then we will adapt to that one, too! With a better understanding of Semantic Search, Google’s Knowledge Graph, conversational search, structured data and semantic keywords research comes the ability to adapt to these changes and provide better results for our clients while keeping you ahead of the curve.


About Mike Ulrich

Mike Ulrich Sr. Manager of Technical SEO at Standing Dog Interactive.
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