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."