What Is a Knowledge Graph? Unlocking the Power of Semantic Search

Key takeaways

• A knowledge graph provides a framework for artificial intelligence (AI) and machine learning (ML) models to store, organize, and make connections between data.

• Google’s search engine uses knowledge graphs, among other tools, to answer factual search queries.

• Besides search engines, a knowledge graph supports applications in recommendation systems, supply chain management, natural language processing, and computer vision.

An LLM knowledge graph helps generate AI responses by connecting entities, relationships, documents, and claims for retrieval-augmented generation (RAG). Learn more about how knowledge graphs power AI applications and careers in three different industries that benefit from this technology in this article. If you’re ready to learn how to use Power BI to connect data sources and transform them into meaningful insights, enroll in the Microsoft Power BI Data Analyst Professional Certificate. Beginner-friendly, this program also covers statistical analysis, snowflake schema, data visualization, and more.

What is a knowledge graph?

A knowledge graph is a way of organizing factual information. The graph creates data entities that represent real-world entities and models how they relate to each other. This includes semantics and definitions that add context, logical constraints on how they relate, and the properties of each object. Organizing this data with a knowledge graph allows a machine learning algorithm to draw information from multiple sources and connect data in context. Knowledge graphs have a flexible design that makes adding new information or context easier, providing an avenue for analyzing relationships between data.

One of the best-known examples of a knowledge graph might be a search engine like Google. If you submit a query that has a factual answer, like “When was Abraham Lincoln born,” the Google algorithm can answer “February 12, 1809” from its knowledge graph displayed in a box above your search results. Google compiles information from various sources and uses knowledge graphs to understand how this data relates. It uses machine learning to understand what real-world entity you’re searching for, summarize the relevant information, and make connections that may not be immediately apparent.

What is an enterprise knowledge graph?

An enterprise knowledge graph draws information specifically from a company or organization's body of knowledge, which can come from PDFs, documents, images, audio recordings, emails, and more. Creating an enterprise knowledge graph gives your organization a central location where everyone in the company can access the same data, so you know your team is working off the same basic facts. A knowledge graph can also help you avoid “siloed” data, or data only accessible by one department or project. This ensures that other teams benefiting from the data can access and use it.

Benefits of a knowledge graph

Knowledge graphs are important because they are foundational to AI. The models use knowledge graphs to store, understand, and make logical conclusions about data. Imagine an AI agent making a recommendation that improves your processes, increases efficiency, or points out a relationship in your data you never noticed before. You’re picturing an AI agent modeling information on a knowledge graph and considering how this data fits together. This technology empowers AI to analyze your systems and processes while making recommendations based on the patterns it finds within data.

Knowledge graphs provide the context behind data patterns, allowing your AI model to make logical connections. For example, you could use an AI model to capture and qualify leads. The model would lead customers through a series of questions, create a knowledge graph to understand their answers, and direct them to the appropriate department or sales staff to meet their needs. The AI model could understand the context of customers' queries, such as whether they have already made a purchase or are considering a purchase, or why they’re interested in a product. This understanding helps the AI to determine which department can help answer their questions or concerns.

Knowledge graphs are flexible and allow you to add data, new definitions, and new context as you gather more information. You can also use a knowledge graph to analyze both structured and unstructured data. Using artificial intelligence to extract the relevant entities and relationships within that data negates the need to sort, structure, and label sources of information. This can help you save time and avoid tedious tasks.

Read more: Structured vs Unstructured Data: What’s the Difference?

What is an LLM knowledge graph?

A large language model (LLM) knowledge graph denotes a graph-based knowledge layer that links entities, relationships, documents, and claims for retrieval-augmented generation, a technique that helps generate AI responses.

What is a knowledge graph used for?

In addition to search engines and enterprise uses, you can use knowledge graphs to apply machine learning to other applications. A few examples of knowledge graph use cases include:

• Recommendation systems and cross-selling: A knowledge graph can help store information about your customers, enabling you to identify patterns and insights to serve them better. For example, a customer could purchase an item, and your knowledge graph determines that customers who purchase this item often buy a second, related item. You can then use this insight to recommend the second item to your customer. The same is true for other types of recommendation systems, such as a streaming platform recommending the next movie you should watch.

• Supply chain management: You can use a knowledge graph to represent data about your supply chain logistics, like supplies, inventory, and transportation. Then you can use algorithms to help you make smart decisions about data, such as the fastest way to deliver your supplies.

• Financial fraud: Financial industry companies can use a knowledge graph to understand customer data. It gives them a mechanism to recognize transactions that don’t follow normal patterns, helping to identify and protect from potential fraud.

• Artificial intelligence: Knowledge graphs create a structure for machine learning and artificial intelligence to extract, organize, and draw new conclusions from data. This is particularly important in areas like natural language processing and computer vision, where an AI model can organize new information and understand relationships in real time.

Who uses knowledge graphs?

Knowledge graphs are foundational to AI and machine learning. As a result, many careers you can explore that use knowledge graphs are rooted in those technologies, like data scientists, software developers, and AI researchers. However, you can also use knowledge graphs in other fields that depend on data science, such as business intelligence and health care research.

All salary information represents the median total pay from Glassdoor as of September 2026. These figures include base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other compensation.

Data scientists

Median annual salary in the US: $159,000 [1]

Job outlook (projected growth from 2025 to 2035): 35 percent [2]

As a data scientist, you work for companies or organizations to understand and unlock insights from data. You determine what data you need for a project and collect, store, and process that data before analyzing the data for data-driven decision-making. In this role, you work with knowledge graphs in the form of algorithms and AI models that can learn the patterns within your data.

Business intelligence analyst

Median annual salary in the US: $117,000 [3]

Job outlook (projected growth from 2025 to 2035): 10 percent [4]

As a business intelligence analyst, you are a data analyst focusing on using business data to improve business processes or otherwise help a company meet its goals. You collect data from relevant sources in your industry, analyze it, and recommend improvements to company leadership. In this role, you work with knowledge graphs in the form of AI applications to help you understand your data and make connections.

Medical research scientist

Median annual salary in the US: $182,000 [5]

Job outlook (projected growth from 2025 to 2035): 13 percent [6]

As a medical research scientist, you design and develop scientific experiments to better understand diseases, pathogens, drug interactions, and more. In this role, you may write scientific papers to share your work with the greater scientific community. You work with knowledge graphs to help you analyze complex data, such as discovering and developing new drugs with AI-powered applications.

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