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)
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:
@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" .
Validation:
Phase 2: Graph Database Selection
Decision Matrix:
Neo4j (Recommended for most):
- Pros: Mature, Cypher query language, graph algorithms, excellent visualization
- Cons: Licensing costs for enterprise, scaling complexity
- Use when: Complex queries, graph algorithms, team can learn Cypher
Amazon Neptune:
- Pros: Managed service, supports Gremlin and SPARQL, AWS integration
- Cons: Vendor lock-in, more expensive than self-hosted
- Use when: AWS infrastructure, need managed service, compliance requirements
ArangoDB:
- Pros: Multi-model (graph + document + key-value), JavaScript queries
- Cons: Smaller community, fewer graph-specific features
- Use when: Need document DB + graph in one system
TigerGraph:
- Pros: Best performance for deep traversals, parallel processing
- Cons: Complex setup, higher learning curve
- Use when: Massive graphs (billions of edges), real-time analytics
Technology Stack:
graph_database: 'Neo4j Community'
vector_integration: 'Pinecone'
embeddings: 'text-embedding-3-large'
etl: 'Apache Airflow'
Neo4j Schema Setup:
CREATE CONSTRAINT person_id IF NOT EXISTS
FOR (p:Person) REQUIRE p.id IS UNIQUE;
CREATE CONSTRAINT org_name IF NOT EXISTS
FOR (o:Organization) REQUIRE o.name IS UNIQUE;
CREATE INDEX entity_search IF NOT EXISTS
FOR (e:Entity) ON (e.name, e.type);
CREATE INDEX relationship_type IF NOT EXISTS
FOR ()-[r:RELATED_TO]-() ON (r.type, r.confidence);
Phase 3: Entity Extraction & Relationship Building
Goal: Extract entities and relationships from data sources
Data Sources:
- Structured: Databases, APIs, CSV files
- Unstructured: Documents, web content, text files
- Semi-structured: JSON, XML, knowledge bases
Entity Extraction Pipeline:
class EntityExtractionPipeline:
def __init__(self):
self.ner_model = load_ner_model()
self.entity_linker = EntityLinker()
self.deduplicator = EntityDeduplicator()
def process_text(self, text: str) -> List[Entity]:
entities = self.ner_model.extract(text)
linked_entities = self.entity_linker.link(entities)
resolved_entities = self.deduplicator.resolve(linked_entities)
return resolved_entities
Relationship Extraction:
class RelationshipExtractor:
def extract_relationships(self, entities: List[Entity],
text: str) -> List[Relationship]:
relationships = []
doc = self.nlp(text)
for sent in doc.sents:
rels = self.extract_from_sentence(sent, entities)
relationships.extend(rels)
valid_relationships = self.validate_relationships(relationships)
return valid_relationships
LLM-Based Extraction (for complex relationships):
def extract_with_llm(text: str) -> List[Relationship]:
prompt = f"""
Extract entities and relationships from this text:
{text}
Format: (Entity1, Relationship, Entity2, Confidence)
Only extract factual relationships.
"""
response = llm.generate(prompt)
relationships = parse_llm_response(response)
return relationships
Validation:
Phase 4: Hybrid Knowledge-Vector Architecture
Goal: Combine structured graph with semantic vector search
Architecture:
class HybridKnowledgeSystem:
def __init__(self):
self.graph_db = Neo4jConnection()
self.vector_db = PineconeClient()
self.embedding_model = OpenAIEmbeddings()
def store_entity(self, entity: Entity):
self.graph_db.create_node(entity)
embedding = self.embedding_model.embed(entity.description)
self.vector_db.upsert(
id=entity.id,
values
