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 — PageFilter that synchronizes frontmatter to the graph on every page save
Knowledge Proposals — Proposal/approval/rejection workflow with frontmatter write-back
Knowledge Admin UI — Three-tab admin panel for proposals, node explorer, and edge explorer
Provenance Model — Three-tier trust model (human-authored → ai-inferred → ai-reviewed)
Frontmatter Conventions — 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

Part_of: FrontmatterConventions, GraphProjector, KnowledgeAdminUi, KnowledgeProposals, ProvenanceModel

Defines: AiAugmentedWorkflows

Requires: McpIntegration, McpIntegration

Enabled by: WikiToMarkdownConverter