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.
實現人工智慧增強的關係知識的知識圖。涵蓋本體設計、圖資料庫選擇(Neo4j、Neptune、ArangoDB、TigerGraph)、實體擷取、混合圖向量架構、查詢模式和人工智慧整合。 在實現知識圖、設計本體、提取實體和關係、選擇圖資料庫或建立混合圖向量搜尋時使用。用於知識圖譜、本體設計、實體解析、圖RAG、幻覺偵測。對於架構選擇和治理,請使用知識庫管理器技能。對於文件檢索管道,請使用 rag-implementer 技能。 來源:oakoss/agent-skills。