What is a Knowledge Graph and how does it work?

Have you ever seen a knowledge card appear in the search results when you Google “current president of the United States” or “weather today”?

If so, you’ve already experienced the power of knowledge graphs in action. The Google Knowledge Graph is a prime example of a widely recognized knowledge graph.

But what exactly is a knowledge graph, and how does it work? How can you leverage it to boost your brand’s visibility and enhance your SEO strategy?

In this comprehensive guide, you’ll learn all of the above.

How Do Knowledge Graphs Influence Search and SEO?

Knowledge graphs enhance SEO by structuring data to improve search engine understanding, enabling rich results, optimizing for voice search, and increasing search rankings, thus boosting visibility and user engagement.

In data science and AI, knowledge graphs improve search results by adding identifiers and descriptions to data to make sense of it, integrate it, and explain it.

Understanding Search Intent

Traditional keyword matching often fails to understand the true intent behind a search query. Knowledge graphs go beyond keywords to grasp the context and relationships between entities.

For example, searching for “famous artist who painted the Mona Lisa” yields the correct result (Leonardo da Vinci) without mentioning his name. This demonstrates how knowledge graphs can interpret the intent behind queries.

Knowledge graphs use natural language processing to understand the context and relationships between entities, enhancing their ability to interpret and respond to complex queries.

Enhancing Voice Search with Natural Language Processing

With the increasing use of voice-activated assistants, natural language voice searches are becoming more common. Knowledge graphs help search engines recognize entities and their attributes in voice queries.

For instance, asking, “Who played the lead in the movie Titanic?” and then “How old is he?” enables the search engine to understand that “he” refers to Leonardo DiCaprio, showcasing the interconnected nature of entities.

Knowledge graphs use machine learning to improve the accuracy of voice search results by training data to feed machine learning algorithms and integrating these techniques to enhance their functionality.

Boosting Brand Visibility and Authority

Data from knowledge graphs often appears in SERP features like knowledge panels and knowledge cards. These features provide significant SERP real estate, enhancing visibility and authority.

If your brand is included in a knowledge graph, your logo and key information can appear in search results, even for non-branded queries, elevating your visibility and perceived authority.

Organizations can leverage internal knowledge graphs by utilizing existing datasets or ontologies to bootstrap their internal knowledge graphs, enhancing brand visibility and authority.

Reducing Click-Through Rates

According to a study by Rand Fishkin, over 50% of searches result in no clicks, partly because knowledge graphs often provide direct answers in the SERP.

While this can reduce organic traffic, focusing on keywords with higher click-through rates can help mitigate this impact.

Knowledge graphs can start from unstructured data, storing vectors along with nodes and relationships to provide long-term memory for AI, which helps deliver direct answers in the SERP.

How to Get Included in a Knowledge Graph

Inclusion in a knowledge graph can significantly enhance your SEO strategy. Here are six comprehensive steps to notably improve your chances of being featured:

Organizing your data in a specific format is crucial for inclusion in a knowledge graph, as it involves linking entity descriptions to one another, forming connections, and creating a graph structure.

Enhance Your PR and Link Building Efforts

Mentions from authoritative sources can signal to search engines that your entity is notable. Although challenging, small and medium businesses can achieve this with a focused PR strategy and scaling link building. Knowledge graphs can also assist with knowledge management tasks such as data governance and fraud detection, enhancing the overall efficiency of organizational units.

Implement Schema Markup on Your Website for Structured Data

Using schema markup (structured data) helps search engines understand your content better. Implement organization markup to include name, logo, URL, and social profiles. Validate your markup to ensure accuracy. A knowledge graph acquires information by integrating it into an ontology and applying a reasoner to derive new knowledge. It organizes entities like concepts, places, people, and organizations and connects them through relationships.

Here’s an example:


{
”@context”: “http://schema.org“,
”@type”: “Organization”,
”name”: “Your Company”,
”description”: “Your Company provides innovative solutions in the tech industry.”,
”url”: “https://yourcompany.com“,
”logo”: “https://yourcompany.com/logo.jpg“,
”email”: “contact@yourcompany.com“,
”address”: {
”@type”: “PostalAddress”,
”addressCountry”: “US”,
”postalCode”: “12345”,
”streetAddress”: “123 Main Street”
},
”founder”: {
”@type”: “Person”,
”name”: “John Doe”,
”gender”: “Male”,
”jobTitle”: “CEO”,
”image”: “https://yourcompany.com/johndoe.jpg“,
”sameAs”: [
”https://twitter.com/johndoe“,
”https://www.linkedin.com/in/johndoe/“
]
},
”foundingDate”: “2005-06-01”,
”sameAs”: [
”https://www.crunchbase.com/organization/yourcompany“,
”https://www.facebook.com/YourCompany“,
”https://www.linkedin.com/company/yourcompany“,
”https://twitter.com/yourcompany“,
”https://www.youtube.com/channel/yourcompany“
],
”contactPoint”: [
{
”@type”: “ContactPoint”,
”contactType”: “customer service”,
”email”: “support@yourcompany.com“,
”url”: “https://yourcompany.com“
}
]

Create a Google My Business (GMB) Profile for Google Knowledge Graph

Creating a GMB profile for a Google Knowledge Graph helps with brand visibility in Google Maps and searches. Ensure that your details (name, address, phone number) are consistent across your website, social media, and GMB listing.

Develop a Wikidata Entry

Wikidata is a structured data repository used by Wikipedia and other projects. A significant portion of data in knowledge graphs comes from Wikidata, making it crucial to have a profile there. To create an entry, follow Wikidata’s Help portal and Notability policy.

Obtain a Wikipedia Page

Having a Wikipedia page can enhance your credibility and presence in knowledge graphs. Ensure that your page adheres to Wikipedia’s guidelines and notability criteria. Ideally, have someone unaffiliated with your brand create the page to avoid bias.

Maintain Consistency

Consistency across all online platforms is vital. Ensure your brand information is uniform across your website, social media profiles, and other digital properties. This consistency helps search engines accurately represent your brand in knowledge graphs.

Semantic Knowledge Graph

A semantic knowledge graph is a graph-based representation of data where entities (nodes) and their relationships (edges) are integrated into an ontology. This means the entities are semantically enriched, providing context and meaning.

For example, a node named "NASH" is ambiguous. However, by aligning it with a disease ontology, the graph clarifies that "NASH" refers to a disease, not a gene, drug, or person. Attributes on nodes and edges further describe their characteristics.

This semantic enrichment allows a knowledge graph to accurately understand and represent real-world entities and their relationships, facilitating more meaningful data analysis and interpretation. Essentially, a knowledge graph understands "things, not strings," enabling computers to comprehend and process data in a human-like context.

Knowledge Graph Examples

Knowledge graphs are powerful tools developed by major tech companies like Google, Microsoft, and Facebook. They significantly impact semantic SEO, entity development, and branding.

Google’s Knowledge Graph: Launched in 2012, it enhances search engine results by understanding the relationships between people, places, and things. This enables Google to provide users with more accurate and relevant information. For instance, searching for a famous person will yield a rich snippet with a brief biography, related people, and key facts, improving user experience and engagement.

Microsoft’s Satori: Microsoft's knowledge graph is integrated into Bing. It helps deliver detailed and structured information right within the search results. Satori enriches Bing’s capabilities by understanding complex queries and presenting concise answers, thus reducing the need for users to click through multiple links.

Facebook’s Graph Search: Although not a traditional search engine, Facebook uses a knowledge graph to connect users with content, people, and interests. By understanding the connections and preferences within its massive user base, Facebook can offer personalized search results and content recommendations, enhancing user engagement and advertising effectiveness.

These knowledge graphs are crucial for SEO as they prioritize structured data and entities, making it easier for search engines to understand and index content. For businesses, this means enhanced visibility, better branding, and a more profound presence in search results, ultimately driving more traffic and engagement.

Conclusion

Understanding and leveraging knowledge graphs is essential for modern SEO. Inclusion in a knowledge graph requires effort and consistency, but the benefits of visibility, authority, and user trust make it worthwhile.

Utilizing knowledge graph AI techniques can significantly enhance these benefits, driving more sophisticated insights and interactions. By following the steps outlined above, you can increase your chances of being featured in a semantic graph and improve your SEO strategy.

Saffron Edge helps your brand stay informed and proactive about these developments, which is key to maintaining a competitive edge in SEO. Knowledge graphs can create new knowledge from existing facts, leading to more relevant search results and actionable insights.