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What is a Knowledge Graph?
A Knowledge Graph is a structured data model that represents entities (such as people, places, concepts, or products) and the relationships between them. It enables systems to understand context, connect information, and deliver more intelligent search, analytics, and recommendations.
Definition
A Knowledge Graph is a networked representation of information where entities are linked by defined relationships to provide contextual understanding.
Key Takeaways
- Connects data through meaningful relationships.
- Enhances search, discovery, and decision-making.
- Widely used in AI, search engines, and enterprise data platforms.
Understanding Knowledge Graphs
Knowledge Graphs move beyond traditional databases by focusing on relationships rather than isolated records. Each entity is a node, and relationships form edges, creating a semantic network that machines can reason over.
They are commonly built using ontologies, schemas, and metadata standards, enabling consistent interpretation across systems. Knowledge Graphs power features such as semantic search, recommendation engines, and question-answering systems.
Enterprises use Knowledge Graphs to unify data silos, improve data governance, and create a single, connected view of information across the organisation.
Formula (If Applicable)
Knowledge Graphs are not formula-based, but are typically modelled using:
- Nodes (entities)
- Edges (relationships)
- Properties (attributes)
- Ontologies or schemas
Real-World Example
Google’s Knowledge Graph enhances search results by showing contextual information about people, places, and topics directly in search.
In enterprises, a Knowledge Graph may connect customers, products, transactions, and support cases to improve analytics and personalisation.
Importance in Business or Economics
Knowledge Graphs improve data intelligence, enable advanced analytics, and support AI-driven decision-making. They help organisations extract value from complex, interconnected data and improve customer experiences.
They are especially valuable in data-rich environments such as finance, healthcare, e-commerce, and logistics.
Types or Variations
- Enterprise Knowledge Graphs: Internal organizational data.
- Public Knowledge Graphs: Open or web-scale graphs.
- Domain-Specific Knowledge Graphs: Focused on a single industry or use case.
Related Terms
- Semantic Web
- Ontology
- Data Graph
- Artificial Intelligence (AI)
Sources and Further Reading
Quick Reference
- Core Idea: Connected data with context.
- Primary Use: Intelligent search and analytics.
- Impact: Better insights and decision-making.
Frequently Asked Questions (FAQs)
How is a Knowledge Graph different from a database?
It focuses on relationships and meaning, not just records.
Are Knowledge Graphs only for large companies?
No, tools now make them accessible to organisations of all sizes.
Do Knowledge Graphs support AI?
Yes, they are foundational to many AI and machine learning systems.
