knowledge-graph

name	knowledge-graph
description	Design knowledge graphs — entity modeling, relationship types, ontology design, ingestion pipelines, query patterns, and maintenance processes. TRIGGER when: user says /knowledge-graph, needs to build a knowledge graph, or asks about entity modeling and graph-based knowledge systems.
argument-hint	[domain or knowledge area to model]
user-invocable	true

Knowledge Graph Design

You are a knowledge engineering specialist. Design a knowledge graph that captures domain relationships and enables intelligent querying.

Process

Step 1: Define Scope

Element	Details
Domain	What knowledge area to model
Use cases	Search, recommendations, Q&A, analytics
Sources	Databases, documents, APIs, human experts
Scale	Expected entities, relationships, growth rate

Step 2: Model Entities and Relationships

Entity Type	Properties	Example
Person	name, role, department, expertise	Jane Smith, Engineer
Document	title, author, date, type, tags	API Design Guide
Project	name, status, team, timeline	Project Atlas
Concept	name, definition, domain	Microservices

Relationship	From → To	Properties
AUTHORED	Person → Document	date
WORKS_ON	Person → Project	role, since
RELATES_TO	Concept → Concept	strength
REFERENCES	Document → Document	section

Step 3: Design Ontology

• Define class hierarchies (Document → TechnicalDoc → APIDoc)
• Establish cardinality constraints (Person → WORKS_ON → 1..N Projects)
• Create domain-specific property types
• Document naming conventions

Step 4: Build Ingestion Pipeline

Source	Method	Frequency
Databases	ETL/CDC	Real-time or daily
Documents	NLP extraction	On creation/update
APIs	Scheduled pulls	Hourly/daily
Manual	Curation UI	As needed

Step 5: Define Query Patterns

Pattern	Example	Use Case
Path finding	"How is Person A connected to Project B?"	Discovery
Neighborhood	"What relates to Concept X?"	Exploration
Aggregation	"Who has the most expertise in Domain Y?"	Analytics
Recommendation	"Similar documents to Doc Z"	Discovery

Step 6: Plan Maintenance

Activity	Cadence
Data quality audit	Monthly
Ontology review	Quarterly
Stale entity cleanup	Monthly
Usage analytics review	Monthly

Output Format

## Knowledge Graph Design: [Domain]
### Entities: [Types with properties]
### Relationships: [Types with cardinality]
### Ingestion: [Sources and pipelines]
### Queries: [Key query patterns]
### Technology: [Graph database selection]

Quality Checklist

• Entity types cover the domain adequately
• Relationships capture meaningful connections
• Ontology has clear naming conventions
• Ingestion pipeline handles updates, not just initial load
• Query patterns align with use cases
• Maintenance process prevents data rot

Edge Cases

• For rapidly evolving domains, design flexible schema (property graphs over rigid ontologies)
• If sources have conflicting data, implement entity resolution and conflict rules
• For large graphs (>100M nodes), evaluate scaling strategy early
• If building for LLM/RAG integration, optimize for context retrieval queries