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What is Semantic SEO: How Search Has Changed and What You Can Do About It

Aug 3, 2020
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Semantic search launched in 2013 with the release of Google's Hummingbird. Since then, Google's search engine has become more complex at a rapid rank. The integration of ML, with RankBrain, and NLP, with B.E.R.T. has enabled the search engine to better understand the context of a query and deliver more personalized and targeted results.

In this video, I share what SEOs need to do today to thrive in the age of Semantic Search. I'll discuss the importance of creating machine-readable content using structured data and the power of leveraging linked open data to help search engines better understand your content.

 

 

🔔  Subscribe for more free YouTube tips: https://www.youtube.com/channel/UC4EW...

🎓  Sign up for our course on Schema.org: https://simplifiedsearch.net/

🎬  An Introduction to Structured Data: How Markup Helps Search Engines Understand Context: https://www.youtube.com/watch?v=uwCR9We3JHw

🎬  How to Use Schema.org to Create and Add Structured Data to Your Website: https://www.youtube.com/watch?v=xQeRA-Ojq5c

🎬  Introducing BERT: Google's New AI Algorithm: https://www.youtube.com/watch?v=Rs_jyNKKt9s

 

Video Transcript: 

So, as I said in the opener, we're going to be discussing semantic SEO, how it applies today, how it's a little different from things that we've been doing in the past, and what we can do today in order to make sure that our sites are optimized for both humans and machines.

What is semantic search? The content on our websites typically are for humans mainly. A lot of the content is human readable only. So, it makes it difficult for a search engine or a crawler to process that information and understand context. There has been, for a very long time, a large gap between what computers can understand and what people can understand. But, over the last few years, a number of advancements have taken place. And, as Google has continued to build and expand their knowledge graph, they're starting to understand things at a much deeper level.

Semantic search is about adding structure and meaning to the content. And it links the data together so that computers can better understand what we're saying. So, when we look at this image from Google image search, you see that the term Jaguar is put in as a query.

Now, this search isn't that old, but even right now, the search engine is having a little difficulty understanding the context of that search. It's not sure if I want to see a vehicle or a South American jungle cat. Right now, the search engine is trying to decide, so it's showing us both. And it's going to change the results based on what I click and what I engage with.

The benefits of the semantic web is helping those crawlers understand the logical links between the queries and what we really understand or what we're expecting from that. And what it's looking at is something called an entity. I've done a video on this before, and I'm going to be doing a lot more around this content in the future, but what an entity is, is it's a thing that exists inside of itself. And it's something inside the database that Google can say, okay, a Jaguar is a car or a Jaguar is a jungle cat. And understanding that entity in the context of the words around it will help it better understand exactly what the user is trying to see and search.

So, most of search today, the crawlers are looking at these terms and they're breaking them down and understanding them and then classifying the specific terms as an entity, using the knowledge that it has about that to make a connection. Semantic search is not new. It's actually been around since about 2013 with the release of Hummingbird. This was Google's first attempt to really return more meaningful answers and begin to understand intent in the context of a query. Again, like we've talked about, it's about creating those relational connections in context. And Hummingbird was a rebuild of Google's base engine that drives search. But it has advanced rapidly over the past few years. So, we've got Hummingbird, which is still in existence. It's kind of  the base engine behind the algorithm. But then, we've had some updates recently, in the last few years, which pushed this context and this understanding a little bit further.

And the first one was RankBrain, which we all know was a machine learning algorithm. Again, it's all about understanding context. And then, just a few months ago, about a year ago, Google released Byrd. And Byrd was a natural language processing tool that was the most advanced of its kind. It's still the most advanced out there right now. And it helps understand the word in context. So, it's understanding both what's coming before and after those entities in order to create a deep understanding.

And then, what these tools do is they go out to the linked open web and look at linked open data and understanding what these entities mean. What do these words mean? What is the context of them? It's pulling in all of this information that has been stored for tens and tens of years and now applying it back to that search query to better understand what the user is looking for. This new approach has radically changed the way that search engines show results specifically in the rich features, like the featured snippets, the people also asked box, the knowledge panel, things of that nature. Those can be influenced very much by a better understanding of the entities on your page from search engines using semantic SEO.

Today's SEOs have to optimize for people and machines. You can't ignore one or the other. You have to optimize around terms that your users are searching for and create content that engages them and meets their needs. This is why I still focus a lot on empathy, and understanding, and the psychological side of our users, because we have to understand why they're searching, what they're searching for, and what they're expecting out of the searches. But then, we also must apply that knowledge to the search engines, and understand what they're looking for, and optimize around entities, and create connections with the knowledge graph and linked open data to create that deeper meaning within our content that the machines themselves can understand. So, we really have to take a look at both of these profiles and personas when we're approaching SEO today.

So, best practices are still in play. Like links, they still play a role. It's still something that will add value to your site that will show that your site has authority and trustworthiness, and still an important factor when it comes to search. Keywords and intent are essential. So, we look at the funnel, understanding how people move through the funnel. And so, we have to play a role. We have to do our keyword, our topic research, and understand the trends, and what people are looking for, and how they're searching for those solutions. And we have to have unique high quality content. We can't just create long form content because it's long form and think it's going to rank. It has to be unique. It has to be high quality. It has to meet and address the needs and the intent of the users.

But, if you want to earn visibility today, you have to first understand how machines interpret entities, understanding, at least at a base level, how natural language processing works, how the search engines are looking at your entities within your pieces of content, what they understand about those, and how they interpret the main focus of your content. We're going to do some videos in the future on this and go a little deeper into some best practices, but this is a fundamental truth that all SEOs need to have at least some understanding in.

And you also need to leverage structured data. That's something we've talked about quite a bit on this channel, and how we can structure our content, structure our pages and add those other layers of information that are for the machines to better understand the content and the context of what we're trying to say.

And lastly, you need to be able to connect your content with linked open data. Linked open data are these massive database resources that Google uses and pulls from within the knowledge graph to better understand what things are, what entities are and creating those connections, help them understand, again, the context of what you're saying in your content.

If you want to begin to leverage this more, I've been spending quite a bit of time, and I'm getting ready to release a course on schema.org markup for rich results. This course is going to help you begin to implement structured data, structured elements on your site. And it's going to give you a deeper understanding of how schema works. And it's going to give you a deeper understanding of how the semantic web works and how you can start to apply these things into your website today.

The course is not live yet as of this point of recording it. But, if you go and you sign up today on our site, we're going to give you a massive discount, a ton of free, cool resources that you can use to start leveraging structured data on your site right away. We're going to be launching in a couple of weeks. And you are also going to get to see this content before anybody else by being a part of our partnership as we launch.

So, if you've got any questions on the content that we spoke about today, linked open data, structured data, semantic SEO, let us know. Comment below. And, if you've got any questions about structured data in general or schema.org, we can also continue that conversation with you. And until next time, Happy Marketing.

Schema.org Rich Features eBook Guide

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Ryan Shelley, CPBI

By Ryan Shelley, CPBI

Ryan is passionate about helping companies make a more personal connection online with their customers and prospects. He is a regular contributor to Search Engine Land, the largest and most popular SEO news site on the web. His works have also been featured on the HubSpot Blog, Business2Community and by LinkedIn Marketing Solutions.

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