Discover how Google's Knowledge Graph works, why it matters for SEO, and how to optimize your content and entities for enhanced search visibility and authority.
Google’s Knowledge Graph is a powerful database of real-world relationships between keywords and the things—people, objects, concepts, etc.—that those search terms represent.
Google’s Knowledge Graph is essentially built on the principles of ontology. An ontology provides the formal framework for defining entities, their attributes, and the relationships between them, ensuring that knowledge is structured in a consistent and machine-readable way. The Knowledge Graph applies this framework at massive scale, linking billions of entities—like people, places, and organizations—through clearly defined relationships.
The Knowledge Graph influences search results in increasingly dynamic ways. As its influence continues to grow, it’s important to understand:
- Use cases for how Knowledge Graph data shows up in the SERPs
- Where Google gets the data to power its Knowledge Graph
- The best ways to prepare your content so it appears in Knowledge Graph–powered search features
Keep reading to learn about the Google Knowledge Graph, and get practical steps on how to make sure your brand and content are represented in this critical component of Google’s search algorithm.
Why the Knowledge Graph is central to modern SEO
Launched in 2012, the Knowledge Graph introduced a radical new approach to delivering search results.
Early search engines, including Google, worked by matching keywords to website text, then sharing a list of matched websites in their search engine results pages (SERPs).
With the introduction of the Knowledge Graph, Google algorithms are better able to identify the people, things, and ideas that searchers really want to find—rather than simply matching search queries to content.
Or as Google has put it, the Knowledge Graph helps searchers find “things, not strings.”
Over time, many of Google’s algorithm updates and expanded SERP features have relied on the Knowledge Graph.
Some of those algorithm updates and SERP features include:
- Semantic search: Google’s ability to understand the meaning and context of searchers’ queries
- Knowledge panels: SERP features that provides short-form facts and information about the searched topic
- Search Generative Experience (SGE): Google’s strategy for delivering AI-powered search results
- Brand visibility and discovery: How brands show up in searches, including how they relate to sub-brands, related brands, industries, products, and more
As Google continues to construct more complex and context-relevant search results, the ability of the Knowledge Graph to match searches to the things they represent becomes more central to SEO.
Do LLMs use Google’s Knowledge Graph?
Large language models (LLMs) like ChatGPT do not have direct access to Google’s proprietary Knowledge Graph during training. Instead, they are trained on large amounts of text data, which allows them to learn patterns of language and meaning.
Researchers often combine the two through approaches like retrieval-augmented generation (RAG), where a model queries a knowledge graph or database at inference time to reduce hallucinations. In other cases, publicly available graphs like Wikidata can be used to fine-tune models or inject structured knowledge. With all that in mind, here’s a closer look at what the Knowledge Graph is and how it works.
With that in mind, here’s a closer look at what the Knowledge Graph is and how it works.
The SEO toolkit you know, plus the AI visibility data you need.
What is the Google Knowledge Graph?
The Google Knowledge Graph is a database of structured data that describes the relationships between different people, places, things, and concepts—collectively known as “entities.”
With the Knowledge Graph, Google has been able to transform its search algorithm from a traditional keyword-match approach to a more logical, entity-based system.
It does this by understanding not only the definitions of words and phrases in its index, but by leveraging its machine learning algorithms to map how those entities and their relationships connect to each other.
For example, a user who searches for “seal” might want to know about any of the following things:
- An identifying emblem—or the object used to create such an emblem
- A member of an elite US Navy unit
- A device or material that prevents leaks
- The world-famous British recording artist
- A semi-aquatic mammal
With the traditional keyword-based system, Google might have shown mixed search listings for pages related to all of the topics that “seal” could represent.
Using the Knowledge Graph, however, Google better understands what entity searchers want to know more about.
By matching searches to entities, Google understands that most people who use the keyword “seal” are looking for the musical artist, rather than the other entities the term “seal” might represent.
This connection becomes more evident when using minor variations in keyword query language.
For example, when searching for “seals,” people are more likely looking for information about semi-aquatic mammals than other things that “seals” could mean.
The Google Knowledge Graph can make these connections because it understands the semantic relationships between words and the real-world entities they represent.
To understand how it does that, let’s dive into an overview of how knowledge graphs work in general.
A quick and dirty knowledge graph primer
Generally speaking, a knowledge graph is a way to represent paired data.
For the purposes of this guide, the paired data being represented is a semantic relationship between two entities.
A semantic relationship refers to how words connect to their meanings. There are three basic parts to such a relationship:
- Subject (noun): The first entity
- Predicate (verb): The relationship between the entities
- Object (noun): The second entity
Semantic relationships are often stated in simple sentences, such as:
A seal has fur.
The semantic relationship here can be broken down as follows:
- Subject: a seal
- Predicate: has
- Object: fur
This relationship is often visualized using circles for the entities (“seal” and “fur”) with an arrow showing the predicate relationship (“has”).
This type of data model is known as a graph—more specifically a directed graph (or digraph) because it shows the direction of the relationship.
Adding more entities (circles) and relationships (arrows) to the graph shows how different entities interact.
Knowledge graphs are used to model data across many different fields, including linguistics, computing, various sciences, and mathematics. In fact, the general concept of a knowledge graph has been around for a long time.
With that in mind, let’s look at how Google’s Knowledge Graph impacts SEO.
Dig deeper: Entities, topics, keywords: Clarifying core semantic SEO concepts
How the Knowledge Graph powers search features
For every search the Google algorithm processes, one of the big decisions it has to make is which SERP features to display.
Part of how it chooses those features is by using its Knowledge Graph to identify what entity (or entities) the user is searching for, as well as what other entities are closely related to it.
For example, in the knowledge panel for the query “seals,” Google provides a number of facts about seals.
The ever-growing complexity and refinement of entities and relationships in the Knowledge Graph has allowed Google to provide more granular and better organized information in its search features.
Here are some examples for the most common search features that illustrate how Google leverages this entity-based information in its results.
Knowledge panels
Knowledge panels contain brief factual information about the main subject (entity) of any given search. They typically appear at the top of mobile search results or on the right of search listings on desktop searches.
The knowledge panel will look similar for many types of searches, though the specific types of factual information may change.
For example, the knowledge panel for recording artist Seal includes:
- How most people know him (“Singer-songwriter and record producer”)
- A short informational paragraph from Wikipedia
- Biographical details
- Relationships to other notable people and groups
Compare that to the knowledge panel for the US Navy SEALs, which shows facts related to the force’s founding, organizational structure, and associated names.
The information included in knowledge panels can come from a few general sources:
- Open data from public sources
- Private data licensed by Google
- Websites Google has crawled that demonstrate a high level of experience, expertise, authoritativeness, and trust (E-E-A-T)
- Directly from the individual, company, or organization—if the panel is claimed by the subject of the knowledge panel
More on how Google sources this data is provided in the section on How Google builds the Knowledge Graph below.
People also searched for (PASF)
Google likes to give users options to refine searches or dig deeper into more specific topics. One of the ways it does this is through a “People also searched for” feature, sometimes shortened to “PASF.”
In fact, many searches may include two sections that are labeled “People also search for”:
- Below the knowledge panel (as shown above)
- At the bottom of the SERP
The PASF feature below the knowledge includes entities that are closely related to the main entity of the search.
For example, when the main search is “navy seals,” the PASF feature includes related military groups.
However, the PASF feature at the bottom of the page tends to be more keyword focused. This PASF is designed to help refine the current search rather than search for different or related entities.
Finally, it’s important to distinguish between “People also search for” (PASF) and “People Also Ask” (PAA):
- PASF helps searchers find the right entity or dig deeper into related entities.
- PAA lets searchers learn more about specific topics related to the current search entity, such as factual information, how-tos, or similar details.
In other words, while both features are likely informed by the Knowledge Graph, PASF has a much more direct link to the entity-based relationships contained in Google’s knowledge base.
Dig deeper: How to maximize visibility in Google’s blended SERPs
Related entities
In addition to some of the SERP features discussed above, Google may use the Knowledge Graph to provide context-specific features with information about related entities.
For example, when searching for the musician Seal, a list of related songs appears near the top of the search results:
There are many different types of entities in the Knowledge Graph. A few such entity types listed in Google’s technical documentation include:
- Book
- Event
- Movie
- Music Recording
- Organization
- Periodical
- Person
- Place
- Sports Team
- TV Episode
- Video Game
More complex versions of some entities are also included, such as Book Series (a sequence of Book entities) or Music Album (a collection of Music Recordings).
Likewise, more specific versions of some entities are included, such as Local Business and Government Organization—each of which is a type of Organization.
Not every type of entity listed here will necessarily show up in the SERPs. The ones that do will be based on the specific context of the search.
For example, musical artists that are on tour may have a calendar of upcoming concerts listed within the search results like the example below.
AI Overviews and featured snippets
Featured snippets have been a part of Google search results for over a decade. However, with the growth of artificial intelligence and the launch of Search Generative Experience (SGE) in 2023, AI Overviews are appearing in a greater share of Google searches.
Both featured snippets and AI Overviews provide searchers with the information they’re most likely to want for the current search.
However, while they serve a similar purpose, these two SERP features function a little differently:
- Featured snippets quote text from a single source that answers or gives information about the query.
- AI Overviews generate a summary of information pulled from multiple sources. They also tend to be longer and cover the topic more broadly than featured snippets.
AI Overviews and featured snippets typically appear at or near the top of search results, and they are larger than standard search listings.
For example, an AI Overview appears at the top of the search “what do seals eat” with information about seal diets and links to multiple sources.
The text for AI Overviews is generated from Google Gemini’s understanding of how the entities are related—in this case, which entity is the eater (seals) and which entities are being eaten (fish, squid, etc.).
Both featured snippets and AI Overviews can also appear in People Also Ask (PAA) and Things to Know.
In the PAA for the same query, the first question provides a featured snippet from a page about seals at the International Fund for Animal Welfare (IFAW).
At the top of the answer, Google identifies the link between the entities of “seal” and “krill” as understood by the Knowledge Graph.
Compare the highlighted text in the search result above and the screenshot of the source page below. The snippet is pulled verbatim from the website, rather than being a generative text overview.
Whether featured snippets remain as a Google SERP staple or go away altogether, both they and their newer AI counterparts will continue to make use of Knowledge Graph in some form.
Further reading: Google’s AI-powered SERPs
Products (honorable mention)
When it comes to product information, Google doesn’t use the Knowledge Graph. Instead, it utilizes a very similar tool known as the Shopping Graph.
That’s because Google uses data submitted through its Merchant Center and Manufacturer Center to populate the Shopping Graph. This type of data is subject to change more frequently, and allowing merchants and manufacturers to update this information means it’s more likely to stay fresh and accurate.
The Shopping Graph powers transactional search results in much the same way that the Knowledge Graph powers searches with informational and other search intents.
For example, when searching for a “wax seal,” Google will display currently available products, including stamps and wax.
Although they are distinct databases, understanding the entity-based approach for both Knowledge Graph and Shopping Graph can help you identify how Google enhances its search results using information in these databases.
How Google builds the Knowledge Graph
As of May 2024, Google had more than 1.6 trillion facts about 54 billion entities in its Knowledge Graph. That was up from 500 billion facts on 5 billion entities in 2020.
And it’s still growing.
So where does Google get all of this data?
Here are some of the ways Google collects and extracts information to power its knowledge base.
Open data and community projects
One of the most substantial ways that Google gathers Knowledge Graph information is through the collection of data from various open-data sources and community projects like the publicly edited online encyclopedia Wikipedia and the related community project, Wikidata.
Wikipedia is the source for many of Google’s features, especially those that pull summary text snippets.
Wikidata, which provides structured-data knowledge base that supports Wikipedia, Wiktionary, and other Wikimedia projects, is also a frequent source for Google Knowledge Graph.
However, Wikidata is rarely mentioned as a primary source in results. But you can see how some of the knowledge panel facts are being pulled from the Wikidata page for that entity, such as in the Navy SEALs example below.
Before Wikidata, Google had used Freebase—its own community-edited database—as a Knowledge Graph source. Freebase was shut down in 2016, and much of its data was transferred to Wikidata.
Government data
Google sources government data to provide specific results.
One such example is The World Factbook published annually by the US Central Intelligence Agency (CIA). The Factbook provides information about countries, geographical areas, organizations, and political alliances around the globe. It pulls its information from various other US agencies, as well as private sources.
The information in the Factbook is considered public domain. This means facts and images from it may also be included in Google’s knowledge graph from other sources, such as this image of a seal pup.
Another example is Google’s structured public data project, Data Commons. This project collects publicly available data from public sources around the world, many of which are governmental agencies or multi-governmental organizations like the United Nations and the European Union.
Although Data Commons has its own separate knowledge graph, Google has acknowledged that some Data Commons data points are used in Google Search as well.
Certain searches can also pull information from government sources related to that topic.
For example, Google pulls weather and air quality data from various national and international meteorological agencies. This data is combined and used to predict weather in Google’s “nowcast” feature.
Private licensed data
Google also licenses data from various private sources to use in its search results.
One example is financial market and exchange data, which Google licenses from private financial data analysts and aggregators like Morningstar, S&P Global, and Intercontinental Exchange.
Similarly, Google works directly with sports leagues and teams, as well as stats aggregators, to get real-time and historical statistics.
For example, Stats Perform says that Google uses its Opta platform to provide real-time sports scores and statistics.
Because Google does not disclose much detail about private data sources, it’s hard to know exactly what information Google pulls from those sources versus its own Knowledge Graph.
Even so, this private data is definitely linked to entities within the Knowledge Graph, and it offers opportunities to find out more about both them and related entities.
Google Books data
Google Books is a massive repository of more than 40 million scanned and digitized books in over 500 languages.
It includes books and book data from a variety of sources, including the Google Books Partner Program and the Google Books Library Project, as well as public and private sources like those mentioned above.
While Google Books is a separate project than Google Search, they are closely aligned. Some Google Books data is included in the Knowledge Graph, and snippets and data from books are used directly in SERP features like knowledge panels.
The same exact highlighted information in the above knowledge panel can be seen in the Google Books page for the book, as shown below.
Structured data from websites
Google recommends using structured data to help its indexing bots understand the layout and content of webpages.
Structured data uses special markup to describe the paired relationships modeled by knowledge graphs. In particular, Google and other search engines use the structured data framework from Schema.org to better understand semantic information on the web.
Webpages with properly marked up schema can have snippets and other information more readily appear in search features like People Also Ask.
The information outlined in the AI Overview above comes from the source which uses schema markup to clarify the relationship to the searched entity.
More information is provided below on how to optimize pages for the Knowledge Graph using schema and other methods.
User feedback and claimed knowledge panels
Users can also influence the information contained in the Knowledge Graph by providing feedback.
When viewing a knowledge panel, you may see the “Feedback” link at the bottom right corner, as highlighted in the following screenshot.
Clicking the link will highlight the various facts and related entities included in the SERPs. Each of these has a flag next to it, allowing the user to provide feedback about that specific item.
After selecting one of these flags, the user can classify their feedback and provide details with supporting evidence, statements, and links.
Users who search for themselves or for someone they represent may be able to claim the knowledge panel after following a verification process. This can provide a greater ability to update (or remove) items and information from appearing in search results.
More information on correcting and claiming knowledge panels is provided below.
Natural Language Processing of unstructured data
Despite all of the efforts out there to find better ways to create and understand structured data, there’s still a large portion of the web that lacks structure.
Unstructured data is information that might be relatively easy for a human to understand, but which is difficult for computers to know.
For example, the following paragraph is easily understood by people:
Seals spend a lot of time swimming, barking at each other, and hunting fish and other seafood. They have fins instead of feet, and their fur helps them stay warm.
In order for a machine to understand the above paragraph, it has to break down each piece of information in that paragraph in a way it can understand.
Such a breakdown might look something like this:
- Entity (subject): Seals:
- Action: Spend time:
- Sub-action: Swimming
- Sub-action: Barking:
- Object: At each other
- Sub-action: Hunting:
- Entity (object): Fish
- Object (object): Other seafood
- Property: Have:
- Entity (object): Fins:
- Clarification: Instead of feet
- Entity (object): Fur:
- Purpose: Stay warm
- Entity (object): Fins:
- Action: Spend time:
Figuring out how to translate human language to machine-understandable models has been problematic since—well, since even before search engines like Google existed.
The solution for this problem is Natural Language Processing (NLP).
With NLP, Google has gotten increasingly better in recent years at being able to move beyond simplistic, keyword-based queries to understanding both queries and indexed pages in more semantically meaningful ways.
Some of Google’s ranking systems that use NLP include:
- Bidirectional Encoder Representations from Transformers (BERT): Helps understand how different word combinations can change the meaning of a keyword or sentence
- Multitask Unified Model (MUM): Improves featured snippet callouts
- Passage ranking: Identifies the meaning and intent of passages within a page of context to provide more relevant results
- RankBrain: Associates words with specific concepts
Google applies these NLP algorithms as part of its search ranking systems, not its indexing systems. That means it’s analyzing the language of both the submitted query and indexed pages to find useful content for users.
Actual use of unstructured data in the Google Knowledge Graph appears to be limited. Rather, Google extracts information from the Knowledge Graph and its indexed webpages based on its NLP understanding of keyword intent.
However, as Google continues to research topics like NLP and its processing of unstructured data gets better and better, it’s more likely that data pulled from unstructured content will find its way into its Knowledge Graph.
Why SEOs should care about the Knowledge Graph
As the Knowledge Graph has grown in size, it has also grown in importance. Over time, new and updated features have leaned further into entity-based information meant to keep people on Google rather than directing users to other web pages.
Older-style search listings are still a big part of the SERPs. However, for some searches, the top listings are getting pushed further and further down the page in favor of Knowledge Graph–powered features like knowledge panels, AI Overviews, and “Things to Know” sections.
Being informed about how the Knowledge Graph can impact the SERPs can help SEOs better understand how to counteract—or even take advantage of—the growing prominence of Knowledge Graph data.
Brand visibility
There are several levels of brand visibility to consider in search results:
- Branded searches: Queries that use the brand name or a commonly used brand nickname (such as “McD” for “McDonald’s”)
- Related entity searches: Queries with closely related people, products, or phrases (such as trademarks and mottoes)
- Related topic searches: Queries about things and ideas that affect the brand’s industry or area of influence (such as health impacts, social concerns, or corporate governance)
Because Google understands the entities behind these searches, it can provide results that connect these entities together.
For example, imagine we are working with the musical artist Seal:
- “Seal” is a branded search term for the artist, because Google understands the main entity behind that search to be the artist. Other branded terms might include keywords like “Seal songs” or “Seal albums.”
- “Batman Forever” is a related entity search in reference to “Seal,” because Google understands the relationship between the Batman Forever soundtrack and Seal’s song on that soundtrack, “Kiss from a Rose.”
- “Songs about flowers” is a related topic search because Google understands Seal’s song “Kiss from a Rose” to be a song about flowers.
Understanding how Google links entities in the Knowledge Graph can help to ensure that the entities and topics you want are connected to your brand. It can also uncover connections between entities you don’t want associated with your brand.
Dig deeper: How to establish your brand entity for SEO: A 5-step guide
E-E-A-T: Experience, Expertise, Authoritativeness, and Trust
Google has long said that it gives preferential ranking to sites that follow E-E-A-T principles. This is also true of information that it includes into the Knowledge Graph.
In fact, some of Google’s updates to the Knowledge Graph appear to focus on including more entities with high E-E-A-T. This makes sense considering that Google is always looking for more and better ways to validate the usefulness and accuracy of the information it indexes across the web.
If Google is pulling data from your website into SERP features powered by the Knowledge Graph, it can be an indication that your site is doing well when it comes to signalling E-E-A-T.
However, if Google isn’t pulling data from your site, it could be a problem with E-E-A-T.
Here are some ways you can try to improve E-E-A-T:
- Experience: Demonstrate your experience, as well as the experience of others who publish on your site, to show that you can back up your content with knowledge and skill.
- Expertise: Highlight your expertise by building content that’s relevant to your industry and closely connected topics.
- Authoritativeness: Display testimonials from others in your field, and showcase any awards or accolades you have that indicate how others perceive you.
- Trustworthiness: This builds on the other three elements in this list, and as you improve your experience, expertise, and authoritativeness, your site will likely be seen as more trustworthy.
Attempting to improve E-E-A-T is always a good choice because it will improve your chances of getting into the Knowledge Graph and ranking higher in search results.
Dig deeper: An SEO guide to understanding E-E-A-T
Voice search and multimodal SEO
As algorithms get more complex and AI becomes more prevalent, more dynamic types of search are emerging:
- Voice search has been available for a while, and it has led to the use of longer-tail, sentence-length search queries.
- Multimodal search is a burgeoning field that mixes voice, image, and audiovisual into a whole new search experience.
With better AI recognition and Natural Language Processing, Google and others are getting better at identifying the entities and relationships in these types of non-text-based searches. It’s becoming less about the specific wording a user utilizes, and more about their intent for the search.
As Google leans into AI-driven multimodal search with products like Google Lens, the need to identify and understand the entities and relationships behind these complex searches will increase.
Click-through rate (CTR) and SERP dominance
Google has always wanted users to stay on Google. And now, with the introduction of AI Overviews and more prominent Knowledge Graph-powered SERP features, traffic to content publishers is plummeting.
There’s a silver lining, however.
Although organic listings are pushed further down the page, AI Overviews tend to have more sources for the information in their generated summaries. These summaries link to prominent pages containing relevant information.
Because there are more sources in AI Overviews, this gives content publishers more opportunities for their pages to appear higher up on the SERPs page—even if they weren’t in the first organic position.
This means that as the Knowledge Graph and AI continue to evolve, it may be better to have content that provides specific information and facts about entities rather than content that ranks at the top of the organic search results.
AI and future search environments
One of the ways that Google is enhancing searches with AI is through what it calls “query fan-out.”
Essentially, this means sending out a bunch of related sub-queries that can be pulled into a single, relevant AI-generated response.
As AI continues to gain greater prominence, finding ways to have your site appear as a source in AI-generated information is going to become more important.
One of the ways to do that is by optimizing for the Knowledge Graph. Read on to see how to do that.
How to optimize for the Knowledge Graph
Knowledge Graph optimization builds on SEO best practices, such as writing useful content relevant to your industry, ensuring a positive user experience, and making sure you’re sending the right technical SEO signals.
In addition, since Knowledge Graph appears frequently in AI Overviews and other AI-generated results, your website must meet Google’s technical requirements for appearing in AI features.
Once you have those bases covered, you’re ready to start optimizing your site for the Knowledge Graph. Let’s take a look at how to do that.
1. Add and test relevant schema
It’s important to mark up your website with the appropriate schema so that Google can clearly and easily understand the information on your site. (See “Structured data from websites above for more information.)
Google Search Central dedicates a section of its documentation to helping webmasters and SEO professionals understand how structured data works in Google Search. Familiarize yourself with it, and keep it handy for reference when implementing schema on your site.
Following are some of the most important schema properties to implement.
Organization: This schema provides details about the main organization behind the website, including logo, description, and contact information that can appear in SERP features. Google recommends adding this only to the site’s home page or a page describing the organization, such as an “About Us” page.
LocalBusiness: This schema is meant to be used if the goal is to show up in Google search as a local business. The LocalBusiness schema would be used for this instead of the Organization schema mentioned above. This allows for additional information like hours of operation and can help you show up in local search results.
ProfilePage: This schema gives information about people who contribute to the website, such as blog authors or contributors. It’s not meant for other types of profiles, such as company leaders, board members, etc. (unless they contribute directly to the website content).
FAQPage: For pages that list Frequently Asked Questions (FAQs), this schema helps answers to those questions appear in search result features like People Also Ask. For sites that feature multiple FAQs across their pages, such as answers to questions on individual product pages, it’s important to make sure that those FAQs are unique, or Google may consider it duplicate content.
QAPage: This schema is for community forums that allow multiple people to respond to a question with comments or threaded conversation. A site would use this schema to mark up the question and multiple answers.
Of course, the above are the bare minimum main schema elements you should be including on your page to optimize it for the Knowledge Graph.
In addition, look through Google’s supported structured data markup and add anything that’s relevant to the type of content you provide and the topics you cover.
Once you’re done adding schema, make sure to check it using Google’s structured data testing tools.
Further reading: Structured data and SEO
2. Audit Wikidata and Wikipedia information
Having a page (or multiple pages) on Wikidata or Wikipedia greatly increases the likelihood of getting information about that topic into the Knowledge Graph.
Unfortunately, adding to and updating these community projects can be incredibly tricky given their renowned hostility toward people editing pages about themselves and related entities.
And doing it wrong can lead to bad press, erosion of brand trust, and a strong public backlash.
For example, The North Face received strong public criticism when it tried to edit its own photos on Wikipedia. And now, the controversy itself is listed on the Wikipedia page about the company.
Before attempting to add or modify content on Wikidata or Wikipedia, consider the following questions:
- Does the person, organization, or information you want to add meet notability guidelines? For example, your cousin Vinny’s local bar gigs might not be generally notable, but if his band gets a big break and goes on tour, he might be able to get a Wikipedia page.
- Can you disclose any and all conflicts of interest? Some conflicts of interest include making edits on behalf of yourself, a relative, a close friend, an employer, and any organizations or affiliations you are associated with now or in the past.
- Are you familiar with the many conduct, content, and other policies that govern the project? Wikipedia has its own complex ecosystem of editorial rules and regulations, such as the need for a neutral point of view and being able to verify facts and claims.
If you answer yes to all of the questions above, you may be able to make a request to create or update a page about yourself, your brand, or an entity or topic related to you or your client.
3. Set up your Google Business Profile
As mentioned above, adding LocalBusiness schema to your local business website is essential. But if you want to really stand out in local search, you should also create and optimize your Google Business Profile (GBP).
GBP gives businesses finer control over how their business, services, and other commercial information appears in search results.
For example, you can:
- Update important information like name, address, and phone number (NAP), multiple locations, and business hours
- Categorize, describe, and provide updates about your business
- Add images and videos
- Provide product information
- Enable reviews and customer messaging
Much of this information is included in knowledge panels and other SERP features. It may also be added to Google’s Knowledge Graph.
Once your GBP is up and running, you can also link your profile to analytics tools like Semrush Local to get additional information about keyword rankings and competitors.
4. Verify government data sources
Sometimes information pulled from government sources can be inaccurate or associated with the wrong entity.
If you find incorrect data from a public source, it might be worth tracking down the right government agency or department to ensure that it gets corrected.
Some types of public data to check include:
- Business incorporation documentation on file with your city, state/province, or national government
- Financial reports and business filings, such as those provided to the Internal Revenue Service (IRS) or Securities and Exchange Commission (SEC), or similar agencies in countries other than the US
- Patent, trademark, copyright, and other intellectual property registrations with regional or national authorities
- Safety, hazard, and recall information submitted to local and national consumer protection watchdogs
Depending on your industry, you may have other public reporting or information requirements that can lead to information about your brand appearing in public data sources.
5. Create social media and community profiles
The more you can control or manage social media pages related to your brand, the more opportunities you have to influence the Knowledge Graph.
That’s because Google keeps track of official social media profiles for some people and brands in the Knowledge Graph. It then displays icons with links to those related profiles in the “Profiles” section of the knowledge panel.
Only selected social media sites can appear in knowledge panels. These include:
- Snapchat
- TikTok
- X (Twitter)
- YouTube
Google has added to this list over time, and it’s possible additional social media profiles could be added to knowledge panels in the future.
In addition, Google may include recent posts by related profiles from some of these social media sites in search results.
You can use the “sameAs” property in Organization, LocalBusiness, and ProfilePage schema on your website to associate your business or personal brand with your social media profiles. You can also add links to selected social media profiles in your Google Business Profile.
Information may also be pulled into the Knowledge Graph from certain business profiles on reputable sites, including:
These are only a few of the most popular sites that publish business profiles. You may be able to find other sites more relevant to your brand and industry, which Google could then use to pull information into the Knowledge Graph and share in SERPs. (See “Structured data from websites” above.)
Business sites often have their own criteria for claiming and managing profiles, and some of them may require a subscription.
6. Ask third-party websites to remove or correct outdated information
While Google prefers fresh content, it’s still possible for content that’s old or no longer relevant to appear in search results.
If you find stale information about you or your company in search results, it may be worth reaching out to the owners of the originating websites to see if they will update or remove it.
You can also publish updates on your own site to help prompt other websites to write new, fresh content with the updated information. While you’re at it, consider removing or refreshing your own old content, as well, to ensure that Google is pulling the most current and relevant information from a source you control.
7. Submit feedback about inaccurate Google Search results
Every Google result comes with the ability to submit feedback on the search.
In the “seals” search results screenshot below, there are two “Feedback” links:
- One at the bottom of the “People Also Ask” feature
- Another in the knowledge panel section
Once you click the feedback link, you can select a general category for the feedback you want to give.
On the next screen, you can further categorize the feedback and provide additional details about the results.
Submitting feedback about a search result isn’t guaranteed to get any results. However, if Google agrees with the feedback, it may update the data in the Knowledge Graph—or even remove it from the search listings altogether.
8. Ensure consistency across your properties
Whether it’s your own website, Google Business Profile, social media, or third-party sites, there’s one way to make sure your information appears accurately in Google’s Knowledge Graph:
Consistency.
One of the reasons Knowledge Graph data may be missing is that conflicting information is preventing Google from knowing what to put in the search results.
The more you can do to check, double check, and triple check your information across your own site and others, the more likely you’ll be able to optimize Knowledge Graph data.
Is there a way to track entity presence and relationships?
Every entity in the Google Knowledge Graph has a unique identifier known by the incredibly creative name “Google Knowledge Graph ID.” It’s sometimes shortened to KGMID.
Wait. Where did the “M” come from?
Great question. It comes from the fact that there are two versions of the KGMID, one that begins with “/m/” and another that starts with “/g/”:
- /m/ signifies an entity that originated in the old Freebase database
- /g/ signifies an entity added to the Knowledge Graph after Freebase was discontinued in 2016
It’s possible to inspect or view the HTML source of a Google search results page to find the KGMID for a given entity. But there’s an easier way to get it.
- Click on the “three dots” at the top of a knowledge panel for the entity.
- Click the “Share” link to copy the shortened URL for the search.
- Paste the shortened URL into your browser’s location bar and click “Enter.”
The shortened URL will redirect to a full Google search URL that looks something like the following:
https://www.google.com/search?kgmid=/m/0gd3n
The URL parameter starting with kgmid= contains the KGMID, which for seals is /m/0gd3n.
Tools for tracking Knowledge Graph entities
It’s okay to go through the manual process above once to fully understand the mechanics of it. However, it would be tedious to find and track entities by KGMID that way all of the time.
Fortunately, there are a few tools that can help.
- Kalicube boasts a huge database of tracked entities, brands, and knowledge panels. It has various levels of service that include consulting on brand visibility, gaining knowledge panels, rebranding, and competitive research.
- InLinks focuses on identifying entity-based gaps, building and marking up content to fill those gaps, and creating contextual interlinking to reinforce entity relationships throughout a website.
- WordLift provides a number of services focused on SEO, engagement, and conversion. Its Visibility solution, in particular, helps identify topics and terminology for creating and marking up entity-based content.
- Google Knowledge Graph Search API is part of Google Cloud, and it can be used on a limited-quota basis for free. Searches using the API return limited information about the entities returned by the query, including name, description, image, and the KGMID.
While there’s no single tool that can provide a magical way to dominate Google’s Knowledge Graph, tools like these can help to better understand where you may already be doing well in providing the right entity-based signals to Google—and where you can improve.
Pro tip: As you build or refresh content on your site, rather than focusing on creating topic clusters around traditional keyword research, consider creating content clusters that focus on entities as topics. You can also find new ways to fill out your content strategy through entity-based competitive analysis.
How to correct or claim a Knowledge Panel
Some knowledge panels can be claimed by the individual or organization they represent.
Never, ever try to claim a knowledge panel for an entity you are not authorized to represent. It’s unethical, and it can endanger your ability to make legitimate claims on knowledge panels in the future.
Claiming a knowledge panel allows you to make updates and provide feedback to features like:
- Featured images
- Titles and subtitles
- Descriptions
- Social media profiles
You can also request that personal information like age, date of birth, and relationship status be removed.
Suggestions submitted by the knowledge panel claimant are more likely to be accepted by Google. However, Google will still have final say in what information gets added to the Knowledge Graph and appears on knowledge panels.
To check if you can claim your knowledge panel:
- Log into your Google account.
- Search for yourself or your organization.
- In the knowledge panel, click on the “three dots” near the top.
- In the dropdown menu that appears, click on “Claim this knowledge panel.”
You may need to go through additional verification procedures to complete the process and fully claim the knowledge panel.
Once you’re verified, you can suggest changes by clicking on the “Suggest edits” in your knowledge panel.
Although edits are not always accepted by Google, claiming a knowledge panel generally gives you a greater ability to influence the information that appears in the panel. Google also typically responds faster (within a few days) for edits suggested by verified individuals as opposed to anonymous users submitting feedback.Plan your content for the Knowledge Graph
The Knowledge Graph is Google’s way of understanding entities and their relationships. As AI and other technologies build on that understanding, it will only become more important.
In other words, relevant, entity-based content is the future and present of SEO.
Now that you’re familiar with how the Knowledge Graph works—and why it’s so important—start working on a content marketing plan that will connect your brand to the right people, products, and ideas.
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