myKG — The Knowledge Graph Engine

myKG automatically generates a confidence-scored knowledge graph from a set of mixed documents — Markdown, plain text, PDF, Word, PowerPoint, Excel, HTML, and images — grounded in an induced RDFS/OWL ontology.

Updates

• v0.4.9 — The nodes.jsonl, edges.jsonl and knowledge_graph.ttl exports now stream to disk record by record instead of building the whole file in memory first, so peak memory stays flat as the graph grows.

• v0.4.8 — mykg_search_nodes and mykg_query_graph's seed selection now use BM25 instead of a linear substring scan, ~2,800x faster at 100K nodes (0.5ms vs. 1.4s per query) and no longer degrading to minutes per query as the graph grows.

Contents

• Features
• Quick Start
• Command line
• Articles & Tutorials
• Configuration
• Troubleshooting
• Extract Pipeline
  • Running
  • Sessions
  • Pipeline Steps
• Outputs
  • Re-running from a Specific Step
  • Orphan-Connection Pass
• Advanced Options
  • Human Review Gate
  • Locked Base Schema
  • SKOS Thesaurus
  • Website / Repo Fetching
  • Standalone Document Conversion
  • Append Mode
    • Incremental Schema Growth
  • Merging Sessions
  • Walkthrough Report
  • Obsidian Vault Export
  • Neo4j LOAD CSV Export
• MCP (Model Context Protocol) Server
• Using mykg with Claude Code
  • Agent mode (Claude Code skill)
  • claude-cli profile
• Roadmap
• Development
  • Testing
  • Healthiness check
• Design
• License

Features

MyKG builds trustworthy knowledge graphs through a self-evolving ontology that continuously adapts, maintains consistency, and assigns confidence scores to knowledge, keeping information grounded and reliable as it grows.

Ontology-Guided Extraction

• Schema-guided knowledge graph generation — the extracted graph is always grounded in a formal RDFS/OWL schema: concept types, property names, domain/range constraints, and the is-a hierarchy are explicit and inspectable before any entity is extracted