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How Knowledge Graphs Impact Search Intent

Jun 28, 2021
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Knowledge graphs are at the heart of search marketing. If we are going to be able to match our user's intent and create compelling content, we need to learn how to leverage knowledge graphs. In this video, I'll share how knowledge graphs impact search intent and what you need to do to make sure your content meets the needs of both users and machines.

 

Video Transcript: 

In the latest episode of Hack My Growth, we're going to talk about knowledge graphs and their impact on search intent. Hey, thanks for checking out this video. If it's your first time watching, or maybe you've been watching a while and you haven't yet hit subscribe, please do so now. We create new content each week to help you get the most out of your digital marketing activities. As I said in the opener, we're going to be talking about knowledge graphs and search intent. If you have any questions along the way, please comment below. We'd love to continue that conversation with you. All right, let's go.

What Is a Knowledge Graph?

In this video, we're going to be looking at knowledge graphs again, and today, we're going to be talking about how they impact search intent. Now I know that sometimes knowledge graphs can be a little bit of a heavy subject and not always the easiest to grasp right off the bat, but I really hope that this video is going to help you really understand how they work, why they're really important and really how they impact search intent and making sure that your content shows for the right types of terms and queries? As a brief introduction, we're just going to cover once again, what a knowledge graph is. A knowledge graph represents a collection of interlinked descriptions of entities, objects, events, or concepts. Knowledge graphs put data into context via linking and semantic metadata and this is a way to provide a framework for data integration, unification, analytics, and sharing. In short, a knowledge graph connects our topics, the things we're talking about, the attributes that make up those different topics, the characteristics, and it links them together in a way that computers can understand so they actually know what something is.

Google has made the shift and they did it a number of years ago, 2012 really, with the release in 2013 of Hummingbird, when it came into real life for many of us. This was the shift from strings to things. Now, Knowledge Graph, capital K, capital G, is talking one hundred percent about Google's specific knowledge graph. Knowledge graphs in general, capital K, lowercase g, are any way that you and I can also represent our information on our website as well. Google uses a knowledge graph, but you can use one too. You can build your own knowledge graph and create structured elements on your website using them. It's important because Google is using them. Google is using them to understand what things mean instead of looking at a string, so when a computer assigns a string, would be text, instead of just saying, okay, let's look at this text. What are other sites that also talk about this text?

How Knowledge Graphs Help Search

They go, okay, what does this concept, what does this thing mean? And who is describing it or presenting the best result for the specific thing? To help really break down this concept of how knowledge graphs work, let's walk through these next couple of slides. I'll show you why they're so important to understanding user intent and making sure that the user gets what they're looking for. I was talking to one of my team members the other day, and we were walking through this concept of knowledge graphs, what they are and why they're important. Now, my team understands they're important. They understand what I'm going for. But a lot of times when I get into a topic like this, I can get a little bit too technical and it confuses people. So I said, "Let's describe your pet."

Now, this person, they love their pet. They think that their pet is really cute. Their pet has short legs. Their pet has a long body. Their pet has floppy ears. Their pet could come in a variety of different colors. You could have a black one, or a black and tan, a cream, a blue and tan. So there's a lot of things this pet could be. Now, if you and I look at this, we are going to make assumptions based on our preconceived notions, our own personal experiences, and our own perceptions. Looking at this, this pet could be a number of things. It could be a dog, it could be a bunny rabbit. What are we actually talking about here? We need more information to really understand, because you and I can go, oh yeah, we know what kind of pet this is. But if we talked about it, we might have completely different concepts.

We need some more information. And this is what knowledge graphs help the search engines understand. If I wrote about my pet and I just said, "My pet is awesome. It has short legs and a long body and floppy ears and it's black and it's really cute," you wouldn't have an idea of what my pet is. And the computer, the search engine, really wouldn't have a clue about what my pet is, so we need some more information. Now we can add another attribute here, another entity, and linking it in here. Well, now we know that our pet is a dog. So again, we still don't have all the information we need, but we've got some more information. So short legs, long body, floppy ears. Now this could be a number of different dogs as well.

So this is why we need to continue to what's called disambiguate what we're talking about. If I said, "I love my dog. It has short legs, a long body, floppy ears, it's super cute and it's cream colored." Now that could be tons of different types of dogs. And if I'm really targeting a specific type, let's say I'm in a niche and I want to make sure that people really, really follow me and want to connect with me because I'm talking about my specific dog, my specific breed of dog, it's important that we continue to add information. So this middle one, instead of saying pet, we've got to get more specific. And in this case, it's a Dachshund, which is what my team member has. She loves her Dachshund. It's got short legs. It's got a long body. It's got floppy ears.

Now here's the crazy thing. We got very specific here and we've got a knowledge graph now, a very simplistic one about a Dachshund. We've got the entity, we've got some attributes describing that entity and we've got other entities that are helping give it more meaning. A Dachshund is a dog. So the next step would be, does this make sense for everybody? Because again, if I went back to this pet, it could be another thing. If I would have given this type of a knowledge graph or this type of understanding to my neighbor, they wouldn't have come up with a Dachshund. They would have come up with a Basset Hound, because these exact same attributes could have described it as well. You can understand how understanding intent can be very difficult for humans, unless we're very specific in what we describe. And imagine a computer that only knows when it's told, the complexity or the confusion that that program has trying to really understand and give the user what they're looking for.

Knowledge Graphs Narrow Search

This is why knowledge graphs are important. If on my page, I explicitly talk about Basset Hound and then underneath that, I have the structured information about the entities that I'm working with, I'm giving them a clear cut understanding of what I'm talking about, who I'm talking to, Basset Hound owners, and the types of things I want to talk about, their color, their body type, those types of things. This is why knowledge graphs are so important, because they make information much more clear. How did the search engines use it? Like we were talking about, search engines look for entities and attributes to disambiguate concepts. They use nodes to connect entities and create either new meaning or more specific meaning. A Dachshund has an attribute called color. Now each of these colors have types that could also be entities like black could be an entity. It's a specific color. Black and tan, we can build these out. And then each of these orange things are nodes.

Make Content Make Sense

Now this is very simplistic and it's not one hundred percent accurate, but it makes a lot of sense when we break it down this way. And this is accurate as we need to be for really understanding the concepts. There are entities, entities have attributes, attributes are connected by nodes. This helps the computers understand the meaning. How do we use this as SEOs or content marketers or anybody that's looking to make sure that our content makes sense. Well, we just flip this a little bit. We should use topics and we need to add characteristics to fully explain what we're talking about. The topic is a Dachshund and Dachshunds come in different colors and they come in all these different colors and we can link these topics together.

Maybe we could say, everything to know about Dachshund, the different colors the Dachshunds can come into. I'm starting to create links and connections to all these different things. I'm creating a deeper meaning. I'm helping the user find the right topic. But if I mark it up as well, I'm going to help the search engines too. I'm actually doing two things at the same time, but we need to start thinking, instead of entities, if that's hard, think of topics. Instead of attributes, think of characteristics. Instead of nodes, think about links. How can you link your topics together? How can you better describe your topics? How can you fully explain the different characteristics around what you do, how you do it, and why it's important? What we have to do to match search intent in this world of semantic search is we have to create content that specifically matches the needs of the user, but it also has to be structured in a way that helps the search engines understand. This allows them to disambiguate to make sure they know exactly what we're talking about.

We do this with structured data. We do this with adding and building that knowledge graph. And for us and our site, we leverage a tool called Word Lift, but there's a lot of ways that you can do it. It just makes it easier for us. But in semantic search, we have to start thinking this way. We have to start thinking, how are we covering topics? Are we matching user intent? Do the users know exactly what we're talking about? And we also have to go to the search engines, know exactly what we're talking about. Semantic search has really shifted the way that we need to approach SEO, and knowledge graphs can help us match user intent better by making it extremely clear what it is we're talking about.

If you want to learn more about semantic search, I just released a new course and it walks you through how to build a knowledge graph, understanding entities, how linked data works, and then it sets you up for building out structured data to take it even further. If you want to check that out, please go to learn.simplifiedsearch.net. And you can always stay up to date with us here on this channel. If you've got any questions about what we talked about, or you want to go deeper into a specific topic, please comment below. And until next time

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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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