Question 1

What is a knowledge graph?

Accepted Answer

A knowledge graph is a structured representation of information that connects entities, relationships, and attributes into a semantic model. It enables systems to understand how data points relate to one another, supporting contextual search, reasoning, and AI applications.

Question 2

How does a knowledge graph improve enterprise search?

Accepted Answer

A knowledge graph enhances enterprise search by modeling relationships between entities rather than relying only on keyword matching. This improves contextual relevance, structured retrieval, and more accurate AI-generated responses.

Question 3

What are the key components of a knowledge graph?

Accepted Answer

Core components include entities, relationships, attributes, an ontology defining structure, and a graph database for storing and querying connected data.

Question 4

What is the difference between hybrid search and a knowledge graph?

Accepted Answer

Hybrid search combines keyword and vector retrieval to find relevant documents. A knowledge graph explicitly models relationships between entities, enabling structured reasoning and relationship-aware intelligence.

Question 5

How do knowledge graphs support AI chat and AI agents?

Accepted Answer

Knowledge graphs ground AI systems in structured entity relationships. This reduces hallucinations, improves response structure, and enables agents to navigate enterprise dependencies with contextual awareness.

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