AI systems require structured knowledge representation to improve reasoning, retrieval, and response accuracy. Two powerful techniques that enhance AI applications are:
- Knowledge Graphs (KGs) – A structured representation of entities and their relationships.
- Knowledge Graph Retrieval-Augmented Generation (KG-RAG) – A hybrid approach that integrates knowledge graphs into RAG pipelines for more accurate and interpretable AI responses.
1. What are Knowledge Graphs?
A Knowledge Graph (KG) is a structured database that stores knowledge as entities and their relationships in a graph format. Unlike traditional relational databases, KGs use nodes (entities) and edges (relationships) to represent real-world information.
Example Knowledge Graph Structure:
| Entity (Node) |
Relationship (Edge) |
Entity (Node) |
| Albert Einstein |
Won |
Nobel Prize in Physics |
| Nobel Prize in Physics |
Awarded in |
1921 |
| Albert Einstein |
Worked at |
Princeton University |
| Princeton University |
Located in |
New Jersey |
How Knowledge Graphs Work
- Nodes (Entities): Represent objects such as people, places, products, or concepts.
- Edges (Relationships): Connect entities with meaningful relationships.
- Attributes (Properties): Store metadata about nodes (e.g., Einstein’s birth year).
- Graph Queries: AI can query the graph to retrieve accurate, structured knowledge.
2. Why Use Knowledge Graphs?
Advantages of Knowledge Graphs
- Structured & Semantic Understanding – Unlike text, KGs store explicit relationships between facts.
- Improved Reasoning & Explainability – AI can traverse the graph to generate explainable answers.
- Factually Correct & Reliable – Helps avoid AI hallucinations by providing fact-verified data.
- Supports Complex Queries – Ideal for multi-hop reasoning (e.g., "Which Nobel Prize winners worked at Princeton?").