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.
实现人工智能增强的关系知识的知识图。涵盖本体设计、图数据库选择(Neo4j、Neptune、ArangoDB、TigerGraph)、实体提取、混合图向量架构、查询模式和人工智能集成。 在实现知识图、设计本体、提取实体和关系、选择图数据库或构建混合图向量搜索时使用。用于知识图谱、本体设计、实体解析、图RAG、幻觉检测。对于架构选择和治理,请使用知识库管理器技能。对于文档检索管道,请使用 rag-implementer 技能。 来源:oakoss/agent-skills。