How Google Uses Knowledge Graphs


Released in 2012, Google Knowledge Graph enables Google Search to return richer search results in response to a user’s search query. 

It allows Google to bundle related data based on information in its massive database containing billions of facts and relationships about various objects like celebrities, movies, space, landmarks, art, cities, etc. 

Mainly, Google Knowledge Graph improves Google Search in three aspects:

  1. Return more relevant and accurate search results.
  2. Summarize content and related facts about a topic. 
  3. Discover unexpected relationships between objects related to a topic.

This article will explain how Google Knowledge Graph works, why it’s useful, and how you can leverage it. But first, let’s understand what a knowledge graph is.

What is a Knowledge Graph?

A knowledge graph is a network representing the relationship between different pieces of related real-world information by structuring data in a more accessible format to answer complex queries.

For example, you have information about a dog, cat, Mustang, wooden table, sofa, wooden desk, and a Ferrari. The knowledge graph would bundle information about the dog and cat in a ‘pets’ category. The Mustang and Ferrari will group in the ‘cars’ category. The wooden table, sofa, and wooden desk will group in the ‘home interior’ category. 

Interestingly, the wooden table and desk will also go in a ‘wooden furniture category’ because the knowledge graph identifies the relationships between similar objects and groups them. 

Understanding How Google’s Search Engine Algorithm Works

On the surface, it’s pretty straightforward. You search for something and get information related to your search query. 

But what’s happening behind the curtains?

To display relevant, helpful information that provides value, Google uses an algorithm to constantly surf the web, update its database, sort it, and use internal quality metrics to determine what should rank in the top search results. 

How Google search works can be categorized into four main parts:

A. Organizing information: Bots known as Crawlers constantly surf through billions of web pages to find all publicly available information on the internet. The information is then organized and stored in Google’s database – the Search Index

B. Ranking results: To determine which web pages should rank against a specific query, Google considers 200+ ranking factors. Some ranking factors carry more weight than others. However, Google does not officially disclose how much weight it assigns to each factor.  

C. Rigorous testing: Constant tests such as live traffic experiments, search quality tests, side-by-side experiments, etc., helps Google display helpful search results. 

D. Detecting spam: Spam-detection algorithms and human labor help detect irrelevant content trying to rank on search results through manipulative practices, such as keyword stuffing and cloaking.

How Google Knowledge Graph Works

Google Knowledge Graph leverages Google’s database to store relevant information together. The most useful information about entities, such as organizations, people, places, or things, is displayed in the Knowledge Panel

The Knowledge Panel is an information box and the front-end visual interface of the Google Knowledge graph. It displays what Google wants you to see from all the information stored in its database. Here’s what it looks like:

Google’s Knowledge Panel

For Google, the Knowledge Graph is most helpful in sorting factual information for which there’s an objective search result. For example: If you search ‘alan turing,’ you get factual information such as a short biography, related books, pictures, etc. 

The knowledge graph also displays a ‘People also search for’ section. It best represents what Google Knowledge Graph has bundled together from various sources and considers most relevant to the search term.

Google encourages users to weigh in on the Knowledge Graph by submitting any changes they think should be made in the Knowledge Panel for a specific search term. Users, particularly developers looking for a more hands-on experience with Google Knowledge Graph, can try out the Knowledge Graph Search API.

The Many Ways Both Google and Users Benefit From the Knowledge Graph

  1. Helps Better Understand User Search Intent

By understanding the relationship between different pieces of information, Google Knowledge Graph captures the intent behind a specific search term. 

For example, say you’re trying to remember a movie you saw a few years ago with space travel where the astronauts traveled through black holes. You can’t seem to remember the name of the movie. 

So, you can search ‘movie about space travel with black holes’ in Google Search. Using the Knowledge Graph, Google can connect the pieces and understand that you’re talking about either the movie Interstellar or Event Horizon. 

Capturing search intent with Google Knowledge Graph

Thus, understanding intent through knowledge graphs helps Google better serve relevant search results. It also helps analyze behavioral patterns, such as whether the intent behind a search term is informational or commercial

  1. Helps Users With Discovery

One exciting feature of the Knowledge Graph is finding information relevant to what you’re searching for. This pops up in the form of ‘People also ask’ and ‘People also search for’ sections against a search term. 

‘Please also ask’ section in Google Search.

‘Please also search for’ section in Google Search.

As a result, you find what you’re looking for, along with related information that Google thinks can provide additional value. Especially if you’re researching something and haven’t reached your destination yet, finding related information is useful.  

  1. Fewer Clicks for Users

When the Knowledge Panel displays all relevant information against a search term, users don’t need to click on additional links. User experience improves and saves time. 

For instance, you search for a term, say, ‘what is ChatGPT,’ and a Wikipedia article pops up in the Knowledge Panel. For most users, clicking on the Wikipedia page from the Knowledge Panel is probably enough. The trouble of going through additional links to find a basic answer is eliminated.

  1. Scalability & Speed

Duplicate content is an issue. If the content is not adding any value or providing further insight around a subject that hasn’t been discussed before, that content is pretty useless.

Duplicate content increases unnecessary information on the internet. It also increases the time needed to scan through all information online. 

Google Knowledge Graph solves this problem. Structuring data based on value makes it easier to scale and avoid serving the user any unnecessary duplicate content. 

Moreover, structured data is also easier to sort. When the user enters a search query, it becomes easier to connect different dots and present a quality search result faster.

Enterprise Knowledge Graphs: How Businesses Can Leverage Google Knowledge Graph

Knowledge graphs are critical for enterprises with a lot of structured and unstructured user data. Industry-scale knowledge graphs deployed by companies such as IBM, Microsoft, Facebook, and eBay, help make intelligent products that interact better with the user. 

All industry-scale knowledge graphs have one thing in common: to make the information available to the users in a more useful format. 

Businesses can even leverage Google’s Enterprise Knowledge Graphs through publicly available APIs, i.e., Entity Reconciliation API and Knowledge Graph Search API.

Google’s Entity Reconciliation API for enterprises is used for deduplication and semantic clustering of tabular data. It leverages a pre-built model based on Google data. The API is intended for enterprise use and can handle graphs with billions of nodes and trillions of edges. 

Google’s Knowledge Graph API leverages data collected by Google and allows you to look up data related to keywords (or entities). It has several features, such as organizing your content based on the existing knowledge graph data and entity resolution – identifying whether multiple data points refer to the same real-world entity.

Enable Smart Research in Life Sciences With Wisecube Knowledge Graph

Knowledge graphs make search smarter. They enable users to find relevant and quality information when there’s data in abundance. The result is better decision-making and insight into what you’re searching for. 

Wisecube Knowledge Graph works in a similar fashion. By applying the technology to the life sciences domain, it helps researchers build connections and find hidden insights from patterns not possible with traditional researching methods. 

Sounds interesting? Schedule a demo with us and find out how Wisecube can enable smarter research using knowledge graphs.

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