Knowledge Graph

What is a Knowledge Graph?

A knowledge graph is a flexible, semi-structured database that organizes data as a network of relationships between entities. Rather than using rigid tables or predefined schemas, knowledge graphs represent information as interconnected nodes and edges, which can describe people, places, systems, documents, or abstract concepts. This structure allows for dynamic querying, rich context, and advanced reasoning.

Knowledge graphs are designed to surface meaning from data by connecting related facts. They make it possible to model complex domains, integrate diverse datasets, and answer questions that span multiple sources. Because of their graph-based design, they can be visualized intuitively and updated without restructuring the underlying data model.

Why Knowledge Graphs are Useful

Traditional databases store data in rows and columns, which can limit their ability to represent relationships between entities. Knowledge graphs overcome this limitation by allowing data to be connected, queried, and explored more naturally.

Key benefits of knowledge graphs include:

• Flexible data modeling with evolving schema support.
  
• Advanced search capabilities using relationships, not just keywords.
  
• Improved data discovery through contextual linking of concepts.
  
• Graph visualizations that reveal patterns and connections.
  
• Better integration of structured and unstructured data.
  

This makes them especially valuable for knowledge management, recommendation systems, data integration, and metadata enrichment.

Core Components of a Knowledge Graph

A knowledge graph typically includes the following components:

• Entities: The “things” being described, such as customers, products, or events.
  
• Relationships: Connections between entities, like “purchased,” “located in,” or “depends on”.
  
• Attributes: Properties or metadata about entities and relationships.
  
• Ontology or schema: A framework that defines the types of nodes and relationships that exist.
  
• Graph database engine: The system that stores and queries the graph efficiently.
  

These components work together to support reasoning, inference, and dynamic exploration of the data.

Use Cases for Knowledge Graphs

Knowledge graphs are used across many industries and domains. Common applications include:

• Enterprise search that retrieves information based on context and relationships.
  
• Data integration from disparate sources with minimal transformation.
  
• Recommendation engines that suggest products, content, or actions based on behavioral links.
  
• Metadata management for connecting business terms, data lineage, and definitions.
  
• Fraud detection by identifying suspicious patterns across connected datasets.
  

Because of their adaptability, knowledge graphs can scale from small departmental projects to enterprise-wide knowledge infrastructures.

Actian and Knowledge Graphs

Actian Data Intelligence Platform supports knowledge graph functionality by connecting technical and business metadata into a unified, contextual view of the data ecosystem. Rather than relying on a separate graph database, Actian builds a knowledge graph using metadata captured from systems, users, and processes across the organization.

Actian’s knowledge graph capabilities allow users to:

• Navigate relationships between data products, owners, and policies.
  
• Understand lineage, quality, and usage in context.
  
• Discover and trust data assets through enriched metadata.
  
• Enable semantic search across business terms, datasets, and governance rules.
  

A knowledge graph is a way of organizing data so that machines can understand relationships between different pieces of information. It’s designed to work in the same way that humans connect ideas. Imagine a web where nodes represent concepts, like “person,” “company,” or “product,” and edges show how those concepts are related, such as “works for,” “owns,” or “manufactures.” This structure enables both people and machines to search, connect, and reason about data more intuitively.

At its core, a knowledge graph is a map of knowledge. It doesn’t just store data. It captures context, or concepts like the “who,” “what,” “when,” “where,” and “how” of information. For example, instead of just listing “Apple” as a company, a knowledge graph can show that Apple was founded by Steve Jobs, is headquartered in Cupertino, and makes the iPhone.

How is a Knowledge Graph Used by Businesses?

Businesses use knowledge graphs to unlock the value of data by making it more connected, contextual, and usable. In today’s data-driven economy, companies face the challenge of dealing with siloed data isolated across different departments and systems. A knowledge graph helps break down those silos and create a unified view.

Here are six common business applications:

1. Customer 360-Degree View

By linking customer interactions, transactions, and behaviors across platforms, companies can build a complete, up-to-date view of each customer. This enables more personalized marketing, proactive customer service, and better sales targeting.

2. Product Information Management

Retailers and manufacturers can use knowledge graphs to organize complex product catalogs, connect related items, and manage specifications across brands and categories.

3. Enterprise Searches

Instead of relying on traditional keyword-based searches, businesses can implement semantic searches using knowledge graphs. This lets employees find information based on meaning and relationships, not just text matches.

4. Fraud Detection

By modeling relationships between users, transactions, and accounts, knowledge graphs help detect suspicious patterns that indicate fraud, such as unusual connections between accounts.

5. Recommendation Systems

Whether it’s for individuals choosing movies, books, or products, knowledge graphs enhance recommendations by understanding the relationships between a user’s preferences and available items.

6. Regulatory Compliance

Businesses in finance, healthcare, and pharmaceuticals use knowledge graphs to track data lineage, manage sensitive information, and comply with regulations like GDPR or HIPAA.

In short, knowledge graphs turn raw data into strategic insights, enabling smarter, faster business decisions.

How is a Knowledge Graph Different From a Traditional Database?

Traditional databases and knowledge graphs both store and manage data, but they serve different purposes and have different structures. Here are five key differences:

1. Structure: Tables vs. Graphs

• Traditional databases, like SQL databases, use tables with rows and columns to store data.
• Knowledge graphs use nodes and edges to represent entities and their relationships.

2. Focus: Data vs. Relationships

• Databases are optimized for storing and retrieving data efficiently.
• Knowledge graphs are designed to model relationships and context, making it easier to perform complex queries across connected information.

3. Schema Flexibility

• Traditional databases require a fixed schema, which means changes can be slow and disruptive.
• Knowledge graphs allow for schema evolution. Users can add new types of data or relationships without redesigning the entire structure.

4. Query Language

• SQL is used to query relational databases.
• SPARQL for RDF graphs or Cypher for property graphs like Neo4j is used to query knowledge graphs.

5. Data Integration

• Knowledge graphs are better suited for integrating diverse data sources, thanks to their flexible and semantic nature.

What are Key Components of a Knowledge Graph?

A knowledge graph is made up of several building blocks that work together to create a rich, semantic network of information. Here are the six most important components:

1. Entities (Nodes)

These are the “things” the graph represents. They can be datapoints like people, organizations, locations, products, etc.

2. Relationships (Edges)

These are the connections between entities, such as “works_for,” “founded_by,” or “located_in.” Relationships are as important as the entities themselves.

3. Properties (Attributes)

Each entity or relationship can have metadata or attributes. For example, a “Person” entity might have attributes such as “name,” “birthdate,” or “email.”

4. Ontology (Schema)

This is the underlying model that defines the types of entities, relationships, and their rules. Ontologies provide consistency, helping both machines and humans understand what each part of the graph means.

5. Triples (for RDF graphs)

In RDF-based graphs, data is stored as triples:

 Subject – Predicate – Object

 For example:

 Steve Jobs – founded – Apple

6. Graph Database or Triple Store

This is the engine that stores and queries the graph. Examples include Neo4j (property graph), GraphDB (RDF), and Amazon Neptune (hybrid).

Together, these components enable knowledge graphs to model complex domains in a highly connected, scalable, and semantically rich way.

How are Knowledge Graphs Used for AI?

Knowledge graphs play a pivotal role in artificial intelligence (AI) by providing structured, interpretable, and explainable knowledge. While AI models like neural networks are often treated as “black boxes,” knowledge graphs offer transparency and reasoning capabilities.

Five key uses for AI include:

1. Knowledge Augmentation for NLP

Natural Language Processing (NLP) applications use knowledge graphs to enhance context. For example, chatbots can reference a knowledge graph to clarify terms, answer questions, or provide consistent definitions.

2. Contextual Reasoning

AI models can use knowledge graphs to make inferences and logical deductions. If a graph knows that “X is a type of Y” and “Y has a feature Z,” it can infer that “X probably has Z.”

3. Semantic Search and Question Answering

Search engines and virtual assistants like Siri or Alexa use knowledge graphs to map user queries to real-world entities and relationships. This improves result accuracy and relevance.

4. Explainability

In machine learning, knowledge graphs help explain why a model made a decision. For example, a recommendation engine might show this reasoning:

“We suggested this book because you liked another book by the same author.”

5. Hybrid AI Systems

Modern AI systems increasingly combine statistical AI, such as neural networks, with symbolic AI like knowledge graphs. This hybrid approach improves robustness, especially in critical areas like healthcare and finance.

In essence, knowledge graphs give AI systems memory, context, and logic, allowing them to reason more like humans.

How is Data Quality Maintained in a Knowledge Graph?

Maintaining data quality in a knowledge graph is crucial because inaccurate or inconsistent data can corrupt relationships and lead to faulty insights. Here are six key strategies used to ensure data integrity:

1. Schema Validation

The graph’s ontology enforces rules about what types of entities and relationships are allowed. Violations can be flagged automatically.

2. Entity Resolution

Also known as deduplication, this involves identifying when different data entries refer to the same real-world entity. For example, “IBM” and “International Business Machines” likely refer to the same company.

3. Data Provenance and Lineage

Tracking where data comes from, or provenance, and how it has changed over time, shown in data lineage, helps ensure trust and accountability.

4. Automated Inference and Consistency Checks

Graphs can use reasoning engines to infer missing data or detect contradictions. For instance, if a person’s birthdate is after their graduation date, the graph can flag it.

5. Curation Tools

Many organizations offer user-friendly interfaces or knowledge graph editors to allow data stewards to manually review and correct graph content.

6. Integration From Trusted Sources

Feeding the knowledge graph with validated, high-quality sources like Wikidata, authoritative databases, or internal master data helps reduce errors at the source.

Quality control in knowledge graphs isn’t just a technical challenge. It’s also a governance and process issue that requires collaboration across teams.

Can a Knowledge Graph Help With Data Governance?

Absolutely. In fact, knowledge graphs are becoming a foundational component of modern data governance frameworks. They help organizations understand, control, and trust their data. 

Here are five ways knowledge graphs support data governance:

1. Data Lineage and Impact Analysis

Knowledge graphs make it easier to track where data comes from, how it’s transformed, and where it’s used. This is essential for audits, troubleshooting, and compliance.

2. Metadata Management

They can integrate and represent metadata, or data about data, in a way that is interconnected and allows for querying. This capability is much more powerful than traditional spreadsheets or wikis.

3. Policy Enforcement

By associating data elements with governance policies, like retention rules or access restrictions, knowledge graphs help enforce compliance automatically.

4. Ownership and Stewardship

Organizations can assign ownership to entities or datasets directly in the graph, ensuring accountability and clear lines of responsibility.

5. Semantic Clarity

Governance is often hampered by inconsistent definitions, such as “What does ‘active customer’ mean?” A knowledge graph captures and shares standardized business terms and definitions across an organization.

Can Knowledge Graphs Work With Large Language Models?

Yes, and this integration is one of the most promising frontiers in AI today. Large Language Models (LLMs) like GPT-4 or Claude are excellent at generating and understanding human language, but they have limitations in accuracy, memory, and reasoning. Knowledge graphs can close those gaps. Here are five ways: 

1. Grounding and Fact-Checking

LLMs can “hallucinate,” or generate false or unverified information. Integrating a knowledge graph allows the model to ground its outputs in verified data, improving factual accuracy.

2. Semantic Search Over Graphs

LLMs can act as a natural language interface to query a knowledge graph. For example, instead of writing SPARQL queries, a user could ask, “Who are all the employees who joined in 2023?” and the LLM can translate that into a graph query.

3. Personalized Responses

With access to a user or product knowledge graph, LLMs can tailor responses more intelligently, taking into account relationships, history, and preferences.

4. Contextual Memory

While LLMs have short-term memory limits, knowledge graphs can act as a long-term contextual memory, helping chatbots and agents remember user preferences or ongoing workflows.

5. Symbolic Reasoning

LLMs excel at language but struggle with formal logic. Knowledge graphs provide structured logic and reasoning paths, enabling hybrid systems to reason more effectively.

Together, knowledge graphs and LLMs offer the potential for deep, explainable, and context-rich AI systems. These are ideal for enterprise applications, research, and intelligent digital assistants.

Actian Data Intelligence Platform: Powered by Knowledge Graph Technology

Knowledge graphs represent a powerful evolution in how organizations model, understand, and use data. Whether a business leader is seeking more value from data, a data scientist is working on explainable AI, or a compliance officer is managing risk, knowledge graphs provide a semantic backbone that connects information, reveals insights, and enables smarter decisions. As they increasingly integrate with AI technologies like LLMs, their potential is only growing.

By automatically linking metadata elements into a connected structure, Actian creates a knowledge graph that is always current and scalable. This helps organizations accelerate insight, enforce governance, and improve collaboration across data teams. Request your personalized demo.