When should I use a knowledge graph vs vector database?

Use graphs when relationships matter (org charts, product hierarchies, interconnected data). Use vectors for semantic similarity (document search, recommendations). Many systems benefit from combining both.

What databases support knowledge graphs?

Popular options: Neo4j (dedicated graph database), Amazon Neptune, Azure Cosmos DB (graph mode), and graph capabilities in PostgreSQL. Choice depends on scale, query patterns, and existing infrastructure.

How do I build a knowledge graph?

Define entity types and relationships for your domain. Extract entities from your data (manually or with AI). Establish relationships. Validate accuracy. Iteratively improve coverage. Start small and expand.

Can LLMs query knowledge graphs?

Yes, through function calling or text-to-query translation. The LLM interprets the user's question, generates a graph query (Cypher, SPARQL), and uses results to answer. This combines reasoning with structured knowledge.

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Knowledge Graph

A network of entities (people, places, concepts) and their relationships, enabling AI to reason about connections and context.

In-Depth Explanation

A knowledge graph is a structured representation of knowledge as a network of entities (nodes) and relationships (edges). Unlike flat document stores, knowledge graphs capture the connections between concepts, enabling sophisticated reasoning.

Knowledge graph structure:

Nodes: Entities (people, products, concepts)
Edges: Relationships (works_for, contains, related_to)
Properties: Attributes of nodes and edges
Types: Categories of entities and relationships

Applications in AI:

Relationship queries: "Who reports to the CEO?"
Path finding: "How are these products connected?"
Recommendation: "Users who bought X also..."
Fact verification: Cross-reference claims
Context enrichment: Add related information to prompts

Knowledge graph vs vector database:

Vectors: Semantic similarity, unstructured
Graphs: Explicit relationships, structured
Best AI systems often use both together

Business Context

Knowledge graphs excel at answering relationship questions like "Which products work together?" or "Who reports to whom?"

How Clever Ops Uses This

We implement knowledge graphs for Australian businesses where relationships matter - product catalogs, organisational structures, and complex domain models.

Example Use Case

"A knowledge graph connecting customers, products, and support tickets to identify patterns and provide contextual support."

Frequently Asked Questions

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Knowledge Graphs & Semantic Search: A Technical Guide

Build intelligent search systems with knowledge graphs. Learn graph database selection, ontology design, entity extraction, and RAG integration with production code examples.

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Knowledge Base KPI