Knowledge Graph

What is a Knowledge Graph?

A Knowledge Graph is a database that represents facts (entities) and their relationships in a network structure. In simple terms, it manages “things” (nodes) such as “people,” “organizations,” and “places,” and the “relationships” (edges) between them in graph form. A real-world example is the information panel displayed on the right side of Google search results for a person’s name, which is powered by a Knowledge Graph. When combined with RAG (Retrieval-Augmented Generation) and Large Language Models, Knowledge Graphs enable AI to generate more accurate and contextually grounded responses.

In a nutshell: Connecting Wikipedia-like databases together so computers can understand relationships like “Taro and Hanako work at the same company.”

Key points:

• What it does: Structure facts and explicitly manage relationships between entities
• Why it matters: AI can understand meaning (semantics) rather than just text matching, enabling more accurate information retrieval and reasoning
• Who uses it: Search engine companies, enterprise information management departments, AI researchers, data analytics teams

Why it matters

Knowledge Graphs are important because they dramatically improve AI understanding accuracy and trustworthiness. With traditional text-based search, when the word “Apple” appears, it’s impossible to determine whether it refers to the fruit or the tech company. However, with a Knowledge Graph, following the “CEO” relationship from the “Apple” entity leads to “Tim Cook,” and following “founded year” leads to “1976,” eliminating ambiguity.

In RAG systems, rather than simple string matching, the system can search for semantically related information on the Knowledge Graph, providing more appropriate context to Large Language Models. This dramatically reduces hallucination—AI generating false information. In business settings, Knowledge Graphs enable centralized management of complex interrelated data like customer information, business partners, and project management, making hidden relationships and anomalies detectable.

How it works

The basic structure of a Knowledge Graph is based on graph theory. Graphs consist of “nodes” (vertices) and “edges” (connections). Nodes represent real-world entities or concepts like “people,” “companies,” and “places.” Edges represent relationships between nodes, such as “lives in,” “works at,” or “owns.”

For example, suppose there are nodes “Taro,” “Tokyo,” “Tech Company XYZ,” and “Hanako,” with the following relationships: “Taro lives in Tokyo,” “Taro works at Tech Company XYZ,” “Hanako also works at Tech Company XYZ,” and “Tech Company XYZ is located in Tokyo.” On the Knowledge Graph, starting from “Taro” and following the “lives in” edge leads to “Tokyo,” and continuing to follow the “workplace” edge reaches “XYZ,” from where you can reach “Hanako.” This structure enables simultaneous management and searching of multiple relationships like “Taro and Hanako’s relationship” and “Taro’s residence.”

Knowledge Graphs are constructed using two main methods. The first is manual entry, where experts and editors explicitly input facts and relationships. This is used when converting Wikipedia or encyclopedias into databases. The second is automatic extraction, where Natural Language Processing (NLP) technology automatically extracts entities and relationships from large volumes of text. For example, from the sentence “Taro works at Tech Company XYZ,” the system automatically identifies entities “Taro” and “Tech Company XYZ” and the relationship “works at.”

For search and reasoning, graph exploration algorithms are used. For the question “Who else works at the same company as Taro?”, the system starts from “Taro,” follows the “employer” edge to identify the entity, and searches for all people related to that company. By incorporating inference rules, the system can also perform logical reasoning such as “if Taro works at X and X is a subsidiary of Y, then Taro belongs somewhere in Y’s organization chart.”

Real-world use cases

Search engine semantic search

When you enter a person’s name in Google search, information about their career, related companies, and achievements appear on the right side. This is an application of Knowledge Graph. When a user searches “Steve Jobs,” the system can immediately present numerous related information (Apple, Pixar, birth year, etc.) through the Knowledge Graph. Additionally, searching “Apple’s founder” allows the system to follow Knowledge Graph relationships and identify “Steve Jobs.”

Enterprise knowledge management

In a large enterprise’s sales department, customers, business partners, projects, and contract terms are complexly interrelated. Using Knowledge Graph enables fast execution of complex queries like “all companies doing business with this customer,” “vendors certified in this technology,” and “everyone involved in this project.” By visualizing hidden relationships, sales teams more easily discover opportunities for new customer acquisition and cross-selling of existing customers.

Healthcare information systems

In healthcare, managing patients, diagnoses, medications, and treatments through Knowledge Graph enables complex searches and reasoning like “what medication is appropriate for this patient’s symptoms?” and “what other medications interact with this medication?” It becomes an important tool supporting physician decision-making.

Benefits and considerations

The greatest benefit of Knowledge Graph is the dramatic improvement in semantic search and reasoning precision. Rather than simple keyword matching, search based on meaning relationships enables more accurate understanding of user intent. Additionally, by structuring complex business knowledge, organizations can more effectively leverage intellectual assets.

However, there are also considerations. Building Knowledge Graphs is time-consuming, and ensuring automatic extraction accuracy is particularly challenging. Since reality constantly changes, Knowledge Graphs require continuous maintenance to stay current. Additionally, entity deduplication (confirming the same thing isn’t called by different names) is technically difficult and requires careful quality management.

Related terms

• RAG — Technology incorporating information searched from Knowledge Graph into Large Language Model prompts
• Semantic Search — Search based on meaning, realized through Knowledge Graph
• Entity Extraction — Technology automatically extracting entities like people and company names from text
• Graph Neural Networks — Neural networks that learn using graph structures as input

Frequently asked questions

Q: What is the difference between a Knowledge Graph and a regular database?

A: Regular databases manage data in table structures with rows and columns, while Knowledge Graphs manage entities and relationships in network form. When dealing with complex cross-referenced data, Knowledge Graphs are more efficient.

Q: To what extent can a Knowledge Graph scale?

A: Tech companies like Google, Facebook, and Amazon operate Knowledge Graphs handling billions of entities and hundreds of billions of relationships. With appropriate graph databases (like Neo4j), substantial scaling is possible.

Q: How is information from Knowledge Graph provided to Large Language Models?

A: Typically, as part of the RAG pipeline, graph query results are converted to text and incorporated into the prompt. This enables the model to provide answers based on current and accurate information.