Kappa Graph — κ(G)

A semantic knowledge graph that extracts concepts from documents, tracks how well-supported they are, and remembers where sources disagree.

κ(G) — vertex connectivity of a graph. The minimum number of connections you'd need to cut before the graph falls apart. A measure of how robust the structure is.

Also kg — the unit of mass. Because knowledge here has weight. Grounding scores measure how heavy an idea is: well-evidenced claims carry more than thin ones. Contested concepts weigh differently than unchallenged ones.

Quick Start

Install Client Tools

The kg CLI, MCP server (for AI assistants), and optional FUSE filesystem:

curl -fsSL https://raw.githubusercontent.com/aaronsb/knowledge-graph-system/main/client-manager.sh | bash

Or just the CLI: npm install -g @aaronsb/kg-cli

Deploy the Platform

Run your own knowledge graph backend:

curl -fsSL https://raw.githubusercontent.com/aaronsb/knowledge-graph-system/main/install.sh | bash

Or from source:

git clone https://github.com/aaronsb/knowledge-graph-system.git
cd knowledge-graph-system
./operator.sh init    # Interactive setup
./operator.sh start   # Start containers

See Quick Start Guide for details.

See It In Action

Interactive graph exploration with smart search, concept clustering, and relationship visualization

Command-line search returns concepts with source images rendered inline via chafa

t-SNE embedding landscape with auto-detected clusters, named by topic via TF-IDF

What You Can Do

Ingest documents — PDFs, markdown, images, text. The system extracts concepts, relationships, and evidence automatically.

Search by meaning — "economic downturn" finds content about recessions, crashes, and crises even if those exact words aren't used.

Explore connections — Find paths between concepts. See how ideas relate across documents.

Check confidence — Every result includes grounding scores. Know what's well-supported vs. contested.

Trace sources — Every concept links back to the original text or image that generated it.

Query via AI — MCP server integration lets Claude and other assistants use the graph as persistent memory.

Navigate via filesystem — Mount the graph as a FUSE filesystem. Use ls, grep, find on semantic space.

Use Cases

Obsidian's graph view rendering knowledge graph relationships via the FUSE filesystem — no plugin needed

Research synthesis — Ingest papers, find connections across them, see where authors disagree. Grounding scores tell you which claims have broad support.

Technical documentation — Extract architecture concepts from diagrams, meeting notes, design docs. Query how components relate.

Agent memory — Give AI assistants persistent, grounded memory. They can check confidence before making claims.

Claude Desktop using MCP to search, explore relationships, and validate claims against the knowledge graph

Spatial understanding — Ingest place photos. The graph lear

... View full README on GitHub