When and how to use knowledge graphs and entities for SEO

If you’re like me, you’ve heard plenty of talk about entity SEO and knowledge graphs over the past year. 

But when it comes to implementation, it’s not always clear which components are worth the effort.

I’ve struggled to determine when and where entity concepts actually impact SEO, especially when time is limited.

While modern search engines are semantic, traditional tactics like keyword research and link building still work well and often overlap with entity-based strategies. 

Through trial and error, I’ve learned when these approaches add value and when they’re just unnecessary extra work.

So, for the practical SEO professional who doesn’t have endless time, how can we effectively leverage entities and knowledge graphs? More importantly, how do we justify the investment?

How to leverage entities and the knowledge graph for SEO 

Many companies and websites are successfully using two powerful entity-SEO strategies:

• Building a knowledge graph for your website
  • Develop a structured knowledge graph to:
    • Establish relationships between new entities.
    • Connect your existing entities to Google’s Knowledge Graph.
• Entity-based internal linking
  • Instead of relying solely on keywords, use entities as anchor text. 
  • This strengthens semantic connections within your site and helps search engines better understand content context.

Before diving into these strategies, let’s explore how they can improve rankings over traditional SEO.

The SEO impact of entity-based strategies

All this extra work can lead to ranking improvements, but only when implemented in the right situations, especially on existing websites rather than those built from scratch.

If you’ve read my previous articles, you know I like using analogies to explain the “why” behind these concepts. 

A strong mental model makes it easier to adapt strategies to different challenges and, most importantly, to determine when and where they’re worth deploying.

Clarity is king 

Imagine you’re evaluating several articles on the SEO changes of 2024. One may stand out, not because it’s flashy, but because it connects new ideas to familiar concepts. 

This intuitive process mirrors how Google operates. When content aligns with what you already know and then builds on it with clear, relatable explanations, it naturally appears more valuable.

From a knowledge graph perspective, imagine overlaying two graphs. The better they align, the more easily a reader can grasp new concepts – especially when those new ideas are linked to familiar ones.

Neuroscience supports this: our brains naturally form networks of knowledge by linking concepts in hierarchies and relationships. 

Think about how the Eiffel Tower immediately brings Paris to mind, then France, and even pastries. 

This is the direction Google is heading – mimicking the human mind by using interconnected knowledge graphs to understand and rank content.

Dig deeper: Entities, topics, keywords: Clarifying core semantic SEO concepts

Search engines aren’t broadly experts

Now, imagine you’re reading a highly technical physics paper, a topic you’re unfamiliar with.

Without a strong background, you’d struggle to make sense of complex ideas unless they’re broken down and connected to what you already know. 

Picture one article that includes a glossary or side notes, seamlessly teaching you new terms by linking them to familiar concepts before using them in context. 

In such a scenario, even without deep subject-matter expertise, you’d find that article much easier to understand and, consequently, more credible.

This extra layer of education and clarity is precisely why content that bridges new ideas with established knowledge tends to be favored by both human readers and search engines like Google.

Expanding on the analogy: Organizing the content for helpfulness

Imagine that, after judging the articles, you’re tasked with organizing them into categories that reflect which prize each one should win. 

This mirrors how Google indexes content and determines which queries it’s relevant for. 

Articles that are clearly explained and tied to well-known subjects are easier to categorize and understand, much like content that aligns with recognized entities. 

As a result, these articles enjoy a wider reach, not just because readers trust them more, but because they can immediately see the connections to what’s relevant to their needs. 

Takeaways

Aligning your site with entities offers two key benefits. 

• Google can serve your content for more queries by clearly understanding its subject. 
• It tends to favor content that is easy to interpret, often assigning it higher quality scores.

These benefits are harder to measure since they don’t appear in clear metrics but function more like qualitative factors – similar to how humans assess communication clarity. 

Their impact extends across individual pages and the entire site, influencing overall quality scores.

While their imprecision can frustrate SEOs, applying these principles can significantly improve rankings. 

Think of it like an essay contest: clarity is crucial to winning, even if it’s just one factor. 

A judge may favor your essay for its clarity but not explicitly cite it as the deciding reason.

When these strategies might not be worth the effort

The good news is that tools are available to implement strategies with knowledge graphs and entities. 

However, while they can certainly help, they aren’t a silver bullet. 

Just as in our essay competition analogy, an article being clearer doesn’t automatically make it better. 

Other articles might have more citations, come from more authoritative sources, or offer more meaningful content that resonates with users.

These strategies are most valuable when clarity can give you an edge or a lack of clarity is holding your site back. 

The benefits won’t be uniform. For some sites, the impact can be huge, while for others it might be minimal. 

This is where an experienced SEO must assess the most pressing issues and allocate their limited time accordingly.

Here are instances where it’s not worth it: 

• Limited content or new sites: Building a comprehensive knowledge graph might be overkill if your website is new or has a small content inventory. The benefits of a detailed semantic structure become more apparent as your content grows.
• Niche or straightforward topics: Traditional strategies might suffice for websites focused on very niche or simple topics. If your content is already clear and easily understood by both users and search engines, the incremental gains from advanced schema or entity linking could be minimal.
• Resource constraints: These strategies require time, technical expertise, and sometimes financial investment in tools or services. If your team is small or already stretched thin, the return on investment might not justify the additional effort.
• Satisfactory existing performance: If your SEO efforts are already driving solid results, you may find that the marginal improvements from these advanced strategies aren’t significant enough to warrant a major overhaul.

Think of these approaches as powerful tools in your arsenal and not a magic wand.

Where is Google headed?

Imagine that Google could hire experts in every field to determine which articles are the highest quality and best serve user intent. 

These experts would start by reading each article, meticulously examining their citations, and then assigning an overall quality score. 

Google’s knowledge graph enables machines to function like these niche experts in many ways. 

As these systems grow more sophisticated, their evaluations will increasingly mirror, and potentially even surpass, the judgments of human specialists.

Dig deeper: Entity-oriented search: The evolution of information retrieval, explained

Now that we understand the impact of entity SEO strategies, let’s explore how each one actually moves the needle, plus tips and tools to help you implement them.

1. Schema markup for external and internal entity connections and disambiguation

Using schema markup, you can connect entities across your site and link them to external references, creating a knowledge graph of your content’s context for search engines.

By consistently adding markup across your pages, crawlers can see how entities relate, forming a connected graph rather than isolated data points.

It’s important to note that Google evaluates schema markup page-by-page. To connect your entities across your site, you must consistently use the @id property. 

(Learn more in this article on ID Schema for SEO, LLMs, and Knowledge Graphs.)

Connecting entities internally the right way 

In this example, each schema is independent, with no explicit connection between related entities. 

As a result, search engines may struggle to understand how these pieces of data relate.

 {   "@context": "https://schema.org",   "@type": "Organization",   "name": "Example Marketing",   "url": "https://example.com" }  {   "@context": "https://schema.org",   "@type": "Person",   "name": "John Doe",   "jobTitle": "CEO",   "worksFor": {     "name": "Example Marketing"   } } 

By using the @id property as shown below, you explicitly link the CEO to the organization. 

This connection creates a unified knowledge graph that clearly shows the relationship between the entities.

 {   "@context": "https://schema.org",   "@type": "Organization",   "@id": "https://example.com/#organization",   "name": "Example Marketing",   "url": "https://example.com" }  {   "@context": "https://schema.org",   "@type": "Person",   "name": "John Doe",   "jobTitle": "CEO",   "worksFor": {     "@id": "https://example.com/#organization"   } } 

Now, search engines understand that “John Doe” is the CEO of “Example Marketing” because the @id reference directly links the two entities.

The right way to connect internal entities to external known sources 

Enhance your site’s clarity by linking entities to trusted sources like Wikidata or Wikipedia, which are part of Google’s Knowledge Graph.

This adds context for search engines and validates your content’s authenticity and relevance.

For example, you can use the sameAs property in your schema markup to link an entity on your site to its corresponding external reference. Consider the following code snippet:

 {   "@context": "https://schema.org",   "@type": "Person",   "name": "John Doe",   "jobTitle": "CEO",   "worksFor": {     "@id": "https://example.com/#organization"   },   "sameAs": [     "https://www.wikidata.org/wiki/Q42",     "https://en.wikipedia.org/wiki/John_Doe"   ] } 

In this example, the sameAs property connects “John Doe” on your site to external sources like Wikidata and Wikipedia. 

(Note: These links are placeholders. Make sure to reference actual, existing pages from both databases.)

Enhancing entity detection with tools

Tools like TextRazor can help you identify key entities on your pages by analyzing the content and providing a list of entities and their relative importance. 

This insight lets you strategically choose which entities to mark up and connect to external references, ensuring your content is as clear and informative as possible.

TextRazor can analyze your content, identify the most important entities on your page, and provide a confidence score for how accurately it maps them to existing entities.)

Automating schema markup

If you prefer automation, WordLift can automatically generate the necessary sameAs markup for your pages using advanced natural language processing (NLP) techniques. 

Alternatively, you can use tools like Screaming Frog in conjunction with JavaScript to generate JSON-LD schema at scale.

Connecting your site’s entities to external references and leveraging automation tools creates a comprehensive, crawlable knowledge graph that enhances your website’s SEO and overall clarity.

2. Internal linking based on entities

Clarifying the relationships between your pages through internal linking is also crucial. 

When you link a page to a broader entity, you’re effectively signaling to search engines that the content is part of an interconnected network. 

This improves clarity and increases the likelihood that your page will rank for a wider range of queries.

Tools like InLinks can automate this process. Still, there’s a lot to gain from a manual approach especially when creating hub-and-spoke content relationships. 

For example, you might:

• Use Wikidata or TextRazor to identify key entities on a page. 
• Select one primary entity representing that page and use it as a natural anchor for internal linking across your site. 

This targeted approach reinforces your content’s contextual relevance and helps search engines better understand how your pages interrelate.

When knowledge graphs and entities matter most

Entity SEO can take many forms, but these two strategies – schema markup and entity-based internal linking – are among the most effective and increasingly popular. 

They help search engines better understand and rank your content by clarifying its structure and relevance.

However, these strategies aren’t universally necessary. Their impact depends on factors like content volume, topic complexity, and available resources. 

For some sites, they can be transformative, especially when clarity is a weak point. For others, traditional SEO methods may still yield the best results.

Rather than viewing entity SEO as a silver bullet, treat it as a strategic tool. 

When combined with proven SEO practices, it can help build a stronger, more semantically rich website that thrives in an evolving search landscape.