knowledge-graph-builder
npx skills add https://github.com/oakoss/agent-skills --skill knowledge-graph-builder
Agent 安装分布
Skill 文档
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
Taskagent to run NER pipelines and build relationship triples - Evaluate graph database options for project requirements: Use
Exploreagent to compare Neo4j, Neptune, ArangoDB, and TigerGraph against scale and query needs - Design ontology and hybrid architecture for a new domain: Use
Planagent to define entity types, relationship schemas, and graph-vector integration strategy - For hybrid KG+RAG systems, delegate to the
rag-implementerskill - For knowledge-graph-powered agent workflows, delegate to the
agent-patternsskill
References
- Ontology Design â Entity types, relationships, properties, RDF schema, validation
- Database Selection â Neo4j, Neptune, ArangoDB, TigerGraph comparison and setup
- Entity Extraction â NER pipeline, relationship extraction, LLM-based extraction
- Hybrid Architecture â Graph + vector integration, hybrid search implementation
- Query Patterns â Cypher queries, API design, common traversal patterns
- AI Integration â KG-RAG, hallucination detection, grounded response generation