Knowledge Graph Builder
Overview
Knowledge graphs make implicit relationships explicit, enabling AI systems to reason about connections, verify facts, and reduce hallucinations. They combine structured entity-relationship modeling with semantic search for powerful knowledge retrieval.
When to use: Complex entity relationships central to the domain, verifying AI-generated facts against structured knowledge, semantic search combined with relationship traversal, recommendation systems, fraud detection, or pattern recognition.
When NOT to use: Simple tabular data (use a relational database), purely document-based search with no relationships (use the rag-implementer skill), read-heavy workloads with no traversal needs, or when the team lacks graph modeling expertise. For KB architecture selection and governance, use the knowledge-base-manager skill.
Quick Reference
| Pattern |
Approach |
Key Points |
| Ontology first |
Define entity types, relationships, properties before ingesting data |
Changing schema later is expensive; validate with domain experts |
| Entity resolution |
Deduplicate aggressively during extraction |
"Apple Inc" = "Apple" = "Apple Computer" must resolve to one entity |
| Confidence scoring |
Attach 0.0-1.0 score + source to every relationship |
Enables filtering by reliability, critical for AI grounding |
| Hybrid architecture |
Graph traversal (structured) + vector search (semantic) |
Vector finds candidates, graph expands context via relationships |
| Incremental build |
Core entities first, validate against target queries, then expand |
Avoid building the full graph before testing with real queries |
| Database selection |
Neo4j (general), Neptune (AWS managed), ArangoDB (multi-model), TigerGraph (massive scale) |
Match database to scale, infrastructure, and query complexity |
Common Mistakes
| Mistake |
Correct Pattern |
| Ingesting entities before designing the ontology |
Define and validate the ontology with domain experts first; changing later is expensive |
| Skipping entity resolution and deduplication |
Deduplicate aggressively so "Apple Inc", "Apple", and "Apple Computer" resolve to one entity |
| Omitting confidence scores on relationships |
Attach a 0.0-1.0 confidence score and source to every relationship |
| Using only graph traversal without vector search |
Implement hybrid architecture combining graph traversal with semantic vector search |
| Building the full graph before validating with real queries |
Start with core entities, test against target queries, then expand incrementally |
| Choosing a database before understanding scale requirements |
Evaluate query patterns, data volume, and infrastructure constraints before selecting |
Delegation
- Extract entities and relationships from unstructured text: Use
Task agent to run NER pipelines and build relationship triples
- Evaluate graph database options for project requirements: Use
Explore agent to compare Neo4j, Neptune, ArangoDB, and TigerGraph against scale and query needs
- Design ontology and hybrid architecture for a new domain: Use
Plan agent to define entity types, relationship schemas, and graph-vector integration strategy
- For hybrid KG+RAG systems, delegate to the
rag-implementer skill
- For knowledge-graph-powered agent workflows, delegate to the
agent-patterns skill
References
- Ontology Design — Entity types, relationships, properties, RDF schema, validation
- Database Selection — Neo4j, Neptune, ArangoDB, TigerGraph comparison and setup
- Entity Extraction — NER pipeline, relationship extraction, LLM-based extraction
- Hybrid Architecture — Graph + vector integration, hybrid search implementation
- Query Patterns — Cypher queries, API design, common traversal patterns
- AI Integration — KG-RAG, hallucination detection, grounded response generation
1---2name: knowledge-graph-builder3description: Implements knowledge graphs for AI-enhanced relational knowledge. Covers ontology design, graph database selection (Neo4j, Neptune, ArangoDB, TigerGraph), entity extraction, hybrid graph-vector architecture, query patterns, and AI integration. Use when implementing knowledge graphs, designing ontologies, extracting entities and relationships, selecting a graph database, or building hybrid graph-vector search. Use for knowledge graph, ontology design, entity resolution, graph RAG, hallucination detection. For architecture selection and governance, use the knowledge-base-manager skill. For document retrieval pipelines, use the rag-implementer skill.4license: MIT5---6
7# Knowledge Graph Builder
8
9## Overview
10
11Knowledge graphs make implicit relationships explicit, enabling AI systems to reason about connections, verify facts, and reduce hallucinations. They combine structured entity-relationship modeling with semantic search for powerful knowledge retrieval.
12
13**When to use:** Complex entity relationships central to the domain, verifying AI-generated facts against structured knowledge, semantic search combined with relationship traversal, recommendation systems, fraud detection, or pattern recognition.
14
15**When NOT to use:** Simple tabular data (use a relational database), purely document-based search with no relationships (use the `rag-implementer` skill), read-heavy workloads with no traversal needs, or when the team lacks graph modeling expertise. For KB architecture selection and governance, use the `knowledge-base-manager` skill.
16
17## Quick Reference
18
19| Pattern | Approach | Key Points |
20| ------------------- | ------------------------------------------------------------------------------------------ | ------------------------------------------------------------------- |
21| Ontology first | Define entity types, relationships, properties before ingesting data | Changing schema later is expensive; validate with domain experts |
22| Entity resolution | Deduplicate aggressively during extraction | "Apple Inc" = "Apple" = "Apple Computer" must resolve to one entity |
23| Confidence scoring | Attach 0.0-1.0 score + source to every relationship | Enables filtering by reliability, critical for AI grounding |
24| Hybrid architecture | Graph traversal (structured) + vector search (semantic) | Vector finds candidates, graph expands context via relationships |
25| Incremental build | Core entities first, validate against target queries, then expand | Avoid building the full graph before testing with real queries |
26| Database selection | Neo4j (general), Neptune (AWS managed), ArangoDB (multi-model), TigerGraph (massive scale) | Match database to scale, infrastructure, and query complexity |
27
28## Common Mistakes
29
30| Mistake | Correct Pattern |
31| ----------------------------------------------------------- | -------------------------------------------------------------------------------------------- |
32| Ingesting entities before designing the ontology | Define and validate the ontology with domain experts first; changing later is expensive |
33| Skipping entity resolution and deduplication | Deduplicate aggressively so "Apple Inc", "Apple", and "Apple Computer" resolve to one entity |
34| Omitting confidence scores on relationships | Attach a 0.0-1.0 confidence score and source to every relationship |
35| Using only graph traversal without vector search | Implement hybrid architecture combining graph traversal with semantic vector search |
36| Building the full graph before validating with real queries | Start with core entities, test against target queries, then expand incrementally |
37| Choosing a database before understanding scale requirements | Evaluate query patterns, data volume, and infrastructure constraints before selecting |
38
39## Delegation
40
41- **Extract entities and relationships from unstructured text**: Use `Task` agent to run NER pipelines and build relationship triples
42- **Evaluate graph database options for project requirements**: Use `Explore` agent to compare Neo4j, Neptune, ArangoDB, and TigerGraph against scale and query needs
43- **Design ontology and hybrid architecture for a new domain**: Use `Plan` agent to define entity types, relationship schemas, and graph-vector integration strategy
44- For hybrid KG+RAG systems, delegate to the `rag-implementer` skill
45- For knowledge-graph-powered agent workflows, delegate to the `agent-patterns` skill
46
47## References
48
49- [Ontology Design](references/ontology-design.md) — Entity types, relationships, properties, RDF schema, validation
50- [Database Selection](references/database-selection.md) — Neo4j, Neptune, ArangoDB, TigerGraph comparison and setup
51- [Entity Extraction](references/entity-extraction.md) — NER pipeline, relationship extraction, LLM-based extraction
52- [Hybrid Architecture](references/hybrid-architecture.md) — Graph + vector integration, hybrid search implementation
53- [Query Patterns](references/query-patterns.md) — Cypher queries, API design, common traversal patterns
54- [AI Integration](references/ai-integration.md) — KG-RAG, hallucination detection, grounded response generation
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