What is a knowledge graph? How it works and real-world examples

Information across an organization is rarely stored in one place. Project requirements live in documents, tasks sit in management tools, and important decisions get recorded in conversations. Understanding how everything connects often takes more effort than finding the information itself.

Knowledge graphs address this challenge by organizing information around entities and their relationships. They help systems understand how people, concepts, documents, and activities connect, making information easier to explore and interpret. In this guide, we'll explain how knowledge graphs work, their key components, benefits, and real-world applications.

What is a knowledge graph?

A knowledge graph is a structured representation of information that connects entities through meaningful relationships. Entities can represent people, places, products, documents, organizations, or abstract concepts. The relationships explain how these entities are associated, creating a network of connected information.

Consider a software development project. A product feature has an assigned engineer, depends on an API, follows requirements documented in a product specification, and belongs to an upcoming release. A knowledge graph can represent each of these elements and the relationships connecting them.

This structure makes it possible to explore questions such as which features depend on a particular API or which requirements are associated with an upcoming release.

Knowledge graphs are particularly useful when information spans multiple systems and understanding the relationships between records is essential. They are used in search engines, recommendation systems, enterprise knowledge management, and AI applications that require structured contextual information.

What are the key components of a knowledge graph?

Knowledge graphs organize information using a small set of building blocks. Each component contributes to how information is represented, connected, and interpreted.

1. Entities (nodes)

Entities represent identifiable objects or concepts within a knowledge graph. In a visual representation, they appear as nodes.

An entity could be a person, company, product, project, document, or software component. Each node represents a specific entity that can be connected to others.

For example, a project knowledge graph might contain separate nodes for a product manager, a feature, an engineering team, and a release milestone.

2. Relationships (edges)

Relationships define how two entities are connected. These connections, called edges, give the graph its structure and contextual meaning.

A relationship might indicate that an engineer owns a task, a feature depends on a service, or a document describes a requirement.

Relationships can also have a direction. For instance, Feature A depends on Feature B communicates a different dependency from Feature B depends on Feature A.

Clearly defined relationships allow applications to follow connections and retrieve information relevant to a particular question.

3. Properties (attributes)

Properties provide additional details about entities and, in graph models that support them, relationships.

A project node might contain a project name, start date, status, and identifier. A dependency relationship could include information about when it was recorded.

Properties make graph data more useful for filtering and analysis. A team could, for instance, identify all features scheduled for a particular release and examine their dependencies.

4. Schemas and ontologies

Schemas and ontologies establish the structure and meaning of information represented in a knowledge graph.

A schema can define which types of entities and relationships are permitted. An ontology provides a more formal description of concepts, their categories, and the relationships between them.

For example, a project ontology might distinguish between employees, teams, projects, and tasks while defining relationships such as assigned to, belongs to, and depends on.

These definitions help maintain consistency when information comes from different systems.

5. Knowledge triples

Knowledge triples express information through three elements: subject, predicate, and object. They are fundamental to the Resource Description Framework (RDF), a widely used model for representing linked information.

Consider the statement:

Payment service → supports → Checkout feature

Here:

• Subject: Payment service
• Predicate: Supports
• Object: Checkout feature

Each triple expresses a relationship or fact. Multiple triples can be combined to represent increasingly detailed information about a domain.

The triple structure is especially relevant to RDF-based knowledge graphs. Other graph models, including property graphs, represent connected information using nodes, edges, and properties.

How does a knowledge graph work?

A knowledge graph works by collecting information, identifying relevant entities, establishing relationships, and organizing the results into a connected structure. Applications can then query that structure to retrieve information or analyze relationships.

The process generally involves six stages.

Step 1: Collect information from multiple sources

The process begins by identifying where relevant information exists.

Organizations might collect data from relational databases, documents, spreadsheets, APIs, business applications, and internal knowledge repositories. These sources can contain structured records as well as unstructured text.

For example, an engineering organization might combine project records, technical documentation, service ownership information, and issue-tracking data.

The data selected depends on the questions the knowledge graph is intended to answer.

Step 2: Identify and resolve entities

Once the information is collected, the system identifies entities that should appear in the graph.

Entity extraction can involve recognizing names, organizations, products, technical components, and other domain-specific concepts. Depending on the source, this can be accomplished through existing database fields, defined rules, or natural language processing.

The system also needs to determine whether different references describe the same entity.

For instance, "Payment API," "Payments Service," and an internal service identifier might refer to the same software component. Entity resolution establishes a consistent identity when the records describe the same object.

Step 3: Define relationships between entities

After identifying entities, the system determines how they are connected.

These relationships can come directly from structured records or be extracted from written information. A service ownership database might explicitly associate a service with a team, while an architecture document could describe which services communicate with one another.

A suitable schema or ontology defines the meaning of these connections.

At this stage, maintaining the distinction between confirmed relationships and inferred relationships is important, particularly when information has been extracted automatically.

Step 4: Construct and store the knowledge graph

The identified entities, relationships, and properties are organized into a graph data model.

Implementations may use RDF-based representations, property graphs, or other suitable approaches. Graph databases are commonly used to store and query connected information, although the underlying storage architecture can vary.

The result is a network in which information can be explored through explicit connections.

Step 5: Query relationships and discover insights

Once the graph is available, applications can query it to retrieve entities, examine connections, and follow relationship paths.

Suppose an engineering manager wants to identify which upcoming features could be affected by a change to an authentication service.

A suitable knowledge graph could connect the service to dependent components, those components to their associated features, and the features to planned releases.

A query could follow these connections to identify potentially affected work.

Some systems also apply reasoning rules to derive additional relationships from existing facts. Such inferences depend on the rules, data quality, and assumptions used.

Step 6: Update and maintain the graph

Knowledge graphs need to reflect changes in the information they represent.

New projects are created, responsibilities change, documents are revised, and software dependencies evolve. Updating the graph keeps its entities and relationships aligned with their underlying sources.

Maintenance can include scheduled data synchronization, validation rules, duplicate detection, and reviews of outdated relationships.

What are the different types of knowledge graphs?

Knowledge graph types are commonly categorized according to the information they represent and the problems they address. These categories can overlap depending on the application.

1. Public knowledge graphs

Public knowledge graphs organize information about widely known entities such as people, places, organizations, historical events, and scientific concepts.

They support applications that need access to broad factual information and relationships between subjects.

Wikidata is an example of a collaboratively maintained source of structured, linked knowledge.

2. Enterprise knowledge graphs

An enterprise knowledge graph connects information across an organization's internal systems and processes.

It may represent employees, teams, customers, products, business activities, and documents, along with the relationships between them.

Enterprise knowledge graphs are useful when different departments maintain information about related entities in separate applications. A connected representation can support cross-system queries, knowledge discovery, and data integration.

3. Domain-specific knowledge graphs

Domain-specific knowledge graphs focus on a particular field or area of expertise.

For example, a healthcare knowledge graph might represent diseases, genes, medications, and clinical concepts. A financial knowledge graph could connect organizations, transactions, accounts, and ownership relationships.

A specialized domain model allows relationships to reflect the terminology and requirements of the field.

4. Product and content knowledge graphs

Product and content knowledge graphs connect products, categories, attributes, topics, and other relevant entities.

An ecommerce application might use these relationships to understand which products serve similar needs or how products relate to particular activities.

Content platforms can also organize articles, authors, topics, and categories to support discovery and navigation.

Why are knowledge graphs important?

The importance of knowledge graphs comes from their ability to preserve relationships between information. This creates opportunities to retrieve relevant context, explore connections, and analyze complex systems.

Here are six key knowledge graph benefits.

1. Connect fragmented information

Organizations often maintain related information across separate tools and repositories. Knowledge graphs can create connections between records from these sources using shared entity identities and defined relationships.

This is valuable when the same project, customer, or business process appears in multiple systems. A similar challenge exists in project execution, where keeping documentation and work items connected helps teams retain the context behind their decisions.

2. Improve contextual search

A knowledge graph can support searches that consider entities and their relationships.

For example, a user looking for documents associated with a particular product might also retrieve information connected through the product's components, owners, or related projects.

The available results depend on how completely the relevant information and relationships have been modeled.

3. Reveal connections across information

Knowledge graphs allow applications to follow multiple relationships and examine how entities are indirectly connected.

In engineering, this could mean tracing a technical dependency across several components. In business analysis, it might involve exploring relationships between organizations and transactions.

These connections provide starting points for investigation and analysis.

4. Support informed decision-making

Decisions frequently depend on understanding which entities or activities will be affected by a change.

A knowledge graph can support this analysis by making relevant dependencies and associations available for querying.

For example, a team evaluating a change to a shared software component could examine the services and features linked to that component before planning the update.

5. Improve organizational knowledge discovery

Important organizational knowledge is often distributed across documents, projects, and people.

A well-maintained knowledge graph can connect these sources, making it easier to identify related information and navigate between concepts.

This becomes useful when teams need to understand historical decisions, locate relevant expertise, or investigate how work relates across departments. Maintaining accessible, organized knowledge also plays an important role in improving team communication through wikis.

6. Provide structured context for AI systems

Knowledge graphs can provide AI applications with explicit information about entities and relationships.

This information can support retrieval, question answering, and tasks requiring connections across multiple facts.

The reliability of an AI response still depends on source quality, retrieval accuracy, and how the application uses the available information.

What are real-world examples of knowledge graphs?

Knowledge graphs are used by established platforms to represent relationships across large collections of information. The following real-world knowledge graph examples show how the same underlying concept supports different applications.

1. Google Knowledge Graph: Connecting information in search

Google uses its Knowledge Graph to organize factual information about entities such as people, places, organizations, and other subjects.

When someone searches for a well-known landmark, Google may display information about its location, history, and other relevant details in a knowledge panel.

The Knowledge Graph helps Google connect factual information from different sources and surface it when relevant to a search.

For example, understanding that a landmark belongs to a particular city allows its location and associated facts to be interpreted within a broader context.

Key application: Organizing factual knowledge to support entity-based search experiences.

2. Wikidata: Creating an open network of structured knowledge

Wikidata is a collaboratively maintained knowledge base that represents information through items, properties, and statements.

An item can represent a city, person, organization, or concept. Properties connect the item to other entities or describe its characteristics.

For instance, a city can be linked to the country it belongs to, its geographical coordinates, and other structured information.

Wikidata also supports qualifiers and references, allowing statements to carry additional context and supporting information.

Its structured model enables data to be queried and reused across languages and applications.

Key application: Maintaining reusable, interconnected factual information for public knowledge projects.

3. LinkedIn Knowledge Graph: Understanding professional relationships

LinkedIn has developed a knowledge graph representing entities within the professional world, including members, jobs, companies, skills, job titles, and educational institutions.

Relationships between these entities help LinkedIn organize professional information and support applications such as search and recommendations.

Consider how a job opportunity can be connected to its required skills, while a member's profile contains information about their professional experience and abilities.

These relationships provide structured signals that can contribute to matching relevant professional information.

LinkedIn's broader Economic Graph also models relationships across workforce and economic data.

Key application: Connecting professional entities to support search, recommendations, and workforce analysis.

4. Amazon COSMO: Representing common-sense shopping knowledge

Amazon researchers developed COSMO, a system designed to construct common-sense knowledge graphs from e-commerce interaction data.

The approach examines signals such as shopping queries and purchasing behavior to identify relationships between products and the purposes they serve.

For example, understanding that a particular product is commonly associated with an activity can provide additional context beyond its basic category or specifications.

COSMO combines language-model-generated hypotheses with automated evaluation and human review to develop higher-quality knowledge assertions.

The resulting approach is intended to support applications such as product discovery and recommendations.

Key application: Representing relationships between shopping needs, activities, and products.

What are the common use cases of knowledge graphs?

Knowledge graph use cases extend across industries that depend on connected information. The following applications demonstrate the kinds of problems graph-based knowledge representation can address.

1. Enterprise knowledge management and search

Organizations can use knowledge graphs to connect internal documents, projects, business entities, and subject-matter expertise.

For example, an employee investigating a previous architecture decision could search for the affected system and follow its connections to design documents, responsible teams, and related decisions.

This approach can make organizational knowledge easier to navigate when relevant information is distributed across repositories. The broader practice of combining project management and knowledge management also helps teams preserve decisions and project context for future use.

2. AI-powered search and knowledge retrieval

Knowledge graphs can supply structured context to AI systems that answer questions using external information.

One approach is GraphRAG, which uses graph-based representations as part of a retrieval-augmented generation workflow.

For example, an AI assistant answering a question about recurring customer issues could use relationships between products, support topics, and documented incidents to retrieve relevant context.

Graph-based retrieval is particularly useful when questions involve related entities or information spread across several documents.

3. Project and engineering dependency analysis

Engineering teams can model relationships between software components, requirements, work items, and releases to investigate dependencies.

Imagine a feature depends on a shared authentication service. That service also supports several other features planned for the same release.

A knowledge graph containing these relationships could help the team identify potentially affected work before introducing a change.

This application becomes valuable when dependencies extend across several teams or technical systems. For teams managing these connections during delivery, dependency mapping in project management provides a practical way to visualize task relationships and their scheduling implications.

4. Personalized recommendations

Recommendation systems can use graph relationships to identify relevant connections between users, products, content, and interests.

For example, a learning platform might connect courses to topics, skills, and prerequisites. These relationships could help the platform recommend courses that align with a learner's existing knowledge and goals.

Graph information can also be combined with behavioral signals and other recommendation techniques.

5. Fraud detection and risk analysis

Financial institutions can use graph-based analysis to investigate relationships among accounts, transactions, devices, and organizations.

A suspicious pattern might involve several accounts connected through the same device or an unusual network of transactions.

Representing these connections makes it possible to examine patterns involving multiple entities.

Such findings can support further investigation, alongside established risk controls and verification processes.

6. Healthcare and scientific research

Knowledge graphs can represent relationships among diseases, symptoms, genes, treatments, and scientific publications.

Researchers may use these connections to investigate biological relationships, explore existing evidence, or identify potential areas for further study.

The usefulness of these applications depends on reliable scientific data, careful interpretation, and appropriate validation.

How is a knowledge graph different from a traditional database or knowledge base?

Knowledge graphs are often discussed alongside relational databases, graph databases, and knowledge bases. Each serves a different role in organizing and accessing information.

Knowledge graph vs. relational database

Relational databases organize data into tables containing rows and columns. Relationships between records are typically represented using keys and retrieved through queries involving joins.

Knowledge graphs represent entities and their relationships directly within a graph structure.

Both approaches can represent connected information. Relational databases are well suited to many structured data and transactional workloads, while knowledge graphs are particularly useful for applications centered on exploring complex, changing relationships.

Knowledge graph vs. graph database

A graph database is a technology designed to store, manage, and query graph-structured data.

A knowledge graph represents information using entities, meaningful relationships, and organizing principles.

A graph database can provide the storage and query capabilities for a knowledge graph. However, a collection of connected nodes and edges becomes a useful knowledge graph through the meaning, consistency, and domain context assigned to its information.

Knowledge graph vs. knowledge base

A knowledge base is a collection of information intended to support reference, learning, or problem-solving.

It might contain help articles, technical documentation, operating procedures, or structured facts. For example, a project wiki can serve as a shared knowledge resource where teams organize requirements, decisions, and technical documentation.

A knowledge graph provides a way to represent knowledge through explicit relationships between entities. A knowledge base can use a knowledge graph internally, and a knowledge graph can reference information stored in a knowledge base.

The following table summarizes the differences.

How to build a knowledge graph step by step

Building a knowledge graph starts with understanding the questions it should answer. The data model, infrastructure, and maintenance process should support that purpose.

Here are six practical steps for creating one.

Step 1: Define the use case and intended outcomes

Identify the problem the knowledge graph should solve.

For example, an engineering organization might want to trace dependencies between services, teams, and product features.

Define the questions users should be able to ask, the information required to answer them, and how the results will be evaluated.

A focused use case helps prevent unnecessary modeling complexity.

Step 2: Identify relevant data sources

Determine which systems contain the required entities and relationships.

Depending on the use case, these might include project databases, service catalogs, documentation repositories, or other business applications.

Assess the availability, completeness, and reliability of the information before integrating it.

Step 3: Design the entity and relationship model

Identify the entity types the graph needs to represent and define the relationships between them.

For a software dependency graph, the model might include services, teams, repositories, and features.

Relationships could describe ownership, implementation, or technical dependencies.

Establish consistent identifiers and add a schema or ontology appropriate to the domain.

Step 4: Prepare and integrate the data

Clean inconsistent records, standardize terminology, and resolve duplicate entities.

Relationships extracted from documents may require additional validation, particularly when automated extraction methods are used.

Where relevant, preserve information about source records and when particular facts were observed.

Step 5: Choose the graph representation and tools

Select a data model and supporting technology based on the information being represented and the queries the system must support.

Common approaches include RDF-based knowledge graphs and property graphs.

RDF-based models use triples and are often queried with SPARQL. Property graph systems generally organize data around nodes, relationships, and properties, with query languages such as Cypher supported by particular platforms.

The choice depends on interoperability needs, data structure, existing infrastructure, and operational requirements.

Step 6: Validate, query, and maintain the graph

Test the knowledge graph using the questions defined at the beginning.

Check whether it retrieves the expected entities, follows relationships correctly, and preserves important contextual information.

Establish processes for monitoring data quality, updating relationships, managing permissions, and resolving conflicting information.

A small, accurate graph that answers a defined set of questions can be more useful than a larger graph with poorly maintained relationships.

What are the challenges and limitations of knowledge graphs?

Knowledge graphs can become difficult to manage as their data sources, relationships, and applications grow. Understanding these challenges helps teams determine where the approach is appropriate.

1. Data quality and consistency

Incomplete or inconsistent source information can introduce incorrect relationships and misleading results.

Organizations need validation rules, clear data ownership, and processes for correcting inaccurate records.

2. Entity resolution

Identifying when multiple records refer to the same entity can be challenging, particularly across independent systems.

Stable identifiers, matching rules, and manual review for ambiguous cases can improve entity resolution.

3. Relationship modeling complexity

A knowledge graph needs meaningful definitions for its entities and relationships.

Models that are too broad may lose useful distinctions, while highly detailed models can become difficult to maintain.

Starting with a focused domain and extending the model gradually can help manage this complexity.

4. Scalability and query performance

Large knowledge graphs can place significant demands on storage and querying infrastructure.

Queries that explore extensive relationship paths may require careful optimization. Performance depends on the graph model, database capabilities, data distribution, and query design.

5. Maintenance and governance

Relationships can become outdated as business processes, ownership, and underlying information change.

Teams need policies for updates, access control, source tracking, and data retention.

These considerations are especially important when knowledge graphs connect sensitive information across organizational systems.

How can Plane help teams organize connected project knowledge?

The same need for connected context appears in everyday project management. Teams need to understand how tasks relate to requirements, which dependencies affect delivery, and where supporting decisions are documented.

Plane brings project work and documentation into a shared workspace. Teams can use work item relations to connect related tasks, dependencies to represent scheduling constraints, and Pages to document requirements, specifications, and decisions. Plane's Wiki also provides a place to organize knowledge that extends across projects. Our guide to Plane's Wiki and Pages explains how these documentation features support different knowledge management needs.

These capabilities help teams maintain useful connections between work and its supporting context. While they serve a different purpose from a semantic knowledge graph, they address a related challenge: keeping project information accessible as teams plan and execute work.

For teams looking to bring greater structure to project execution and documentation, explore Plane or talk to sales to learn more.

Conclusion

Knowledge graphs provide a structured way to represent entities and understand the relationships connecting them. Their applications range from factual search and recommendations to scientific research, enterprise knowledge management, and AI-assisted information retrieval.

For organizations working with information distributed across multiple systems, the value comes from making relevant connections accessible and meaningful. A well-designed knowledge graph can provide the context needed to investigate relationships, discover information, and support better-informed decisions.

Frequently asked questions

1. What is a knowledge graph in simple terms?

A knowledge graph organizes information as connected entities and relationships. It represents objects such as people, products, or documents and shows how they relate to one another, allowing systems to explore and retrieve contextual information.

2. What is the difference between a knowledge graph and an ontology?

An ontology defines concepts, categories, and relationships within a domain. A knowledge graph represents entities and their connections using structured information. Ontologies can provide the definitions and rules used to organize a knowledge graph.

3. Is a knowledge graph the same as a graph database?

A knowledge graph represents meaningful relationships between entities, while a graph database provides technology for storing and querying graph-structured data. Graph databases are commonly used to implement knowledge graphs.

4. How are knowledge graphs used in artificial intelligence?

Knowledge graphs provide AI systems with structured information about entities and relationships. They can support contextual retrieval, question answering, and graph-based retrieval-augmented generation workflows, including GraphRAG.

5. What is the difference between a knowledge graph and a knowledge base?

A knowledge base stores information such as documents, articles, or structured records. A knowledge graph represents knowledge through explicit relationships between entities. The two can work together to support information retrieval and discovery.

6. Can knowledge graphs be created from unstructured documents?

Yes. Knowledge graphs can be constructed from documents using techniques such as natural language processing and entity extraction. These methods identify relevant entities and relationships, which are then organized, validated, and stored in a graph structure.

7. What tools and technologies are used to build knowledge graphs?

Knowledge graphs can be built using RDF frameworks, graph databases, entity extraction tools, and data integration pipelines. Common technologies include Neo4j, Apache Jena, RDF, SPARQL, and Cypher, depending on the chosen data model and implementation requirements.