Knowledge Graph Core

The knowledge graph is a semantic layer over wiki content that enables AI agents to discover relationships, traverse connections, and propose new knowledge — all grounded in human-authored content.

Architecture

Four PostgreSQL tables form the graph:

| Table | Purpose |

|-------|---------|

| `kg_nodes` | Entities (wiki pages, concepts, stubs) with JSONB properties |

| `kg_edges` | Typed relationships between nodes with provenance tracking |

| `kg_proposals` | Pending AI-suggested enrichments awaiting human review |

| `kg_rejections` | Negative knowledge preventing re-proposals |

Sub-Features

- **[GraphProjector](GraphProjector)** — PageFilter that synchronizes frontmatter to the graph on every page save

- **[Knowledge Proposals](KnowledgeProposals)** — Proposal/approval/rejection workflow with frontmatter write-back

- **[Knowledge Admin UI](KnowledgeAdminUi)** — Three-tab admin panel for proposals, node explorer, and edge explorer

- **[Provenance Model](ProvenanceModel)** — Three-tier trust model (human-authored → ai-inferred → ai-reviewed)

- **[Frontmatter Conventions](FrontmatterConventions)** — Rules for how frontmatter keys become properties vs. edges

MCP Integration

Two separate MCP endpoints serve different use cases:

- `/mcp` (authoring) — 3 knowledge tools: `propose_knowledge`, `list_proposals`, `list_rejections`

- `/knowledge-mcp` (consumption) — 5 read-only tools: `discover_schema`, `query_nodes`, `traverse`, `get_node`, `search_knowledge`

[{Relationships}]