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.
  

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.