Knowledge Graph Builder

Knowledge Graph Builder

Build structured knowledge graphs for enhanced AI system performance through relational knowledge.

Core Principle

Knowledge graphs make implicit relationships explicit, enabling AI systems to reason about connections, verify facts, and avoid hallucinations.

When to Use Knowledge Graphs

Use Knowledge Graphs When:

• ✅ Complex entity relationships are central to your domain
• ✅ Need to verify AI-generated facts against structured knowledge
• ✅ Semantic search and relationship traversal required
• ✅ Data has rich interconnections (people, organizations, products)
• ✅ Need to answer "how are X and Y related?" queries
• ✅ Building recommendation systems based on relationships
• ✅ Fraud detection or pattern recognition across connected data

Don't Use Knowledge Graphs When:

• ❌ Simple tabular data (use relational DB)
• ❌ Purely document-based search (use RAG with vector DB)
• ❌ No significant relationships between entities
• ❌ Team lacks graph modeling expertise
• ❌ Read-heavy workload with no traversal (use traditional DB)

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6-Phase Knowledge Graph Implementation

Phase 1: Ontology Design

Goal: Define entities, relationships, and properties for your domain

Entity Types (Nodes):

• Person, Organization, Location, Product, Concept, Event, Document

Relationship Types (Edges):

• Hierarchical: IS_A, PART_OF, REPORTS_TO
• Associative: WORKS_FOR, LOCATED_IN, AUTHORED_BY, RELATED_TO
• Temporal: CREATED_ON, OCCURRED_BEFORE, OCCURRED_AFTER

Properties (Attributes):

• Node properties: id, name, type, created_at, metadata
• Edge properties: type, confidence, source, timestamp

Example Ontology:

# RDF/Turtle format
@prefix : <http://example.org/ontology#> .

:Person a owl:Class ;
    rdfs:label "Person" .

:Organization a owl:Class ;
    rdfs:label "Organization" .

:worksFor a owl:ObjectProperty ;
    rdfs:domain :Person ;
    rdfs:range :Organization ;
    rdfs:label "works for" .