mykg
Converts unstructured documentation into queryable knowledge graphs, enabling teams to surface hidden relationships, enforce consistency, and automate downst…
myKG generates a confidence-scored knowledge graph from mixed documents — Markdown, plain text, PDF, Word, PowerPoint, Excel, HTML, and images — grounded in an induced RDFS/OWL ontology. Works with Anthropic, OpenAI, OpenRouter, or local Ollama models, and ships both an MCP server and a Claude Code skill mode so extractions can run inside a Claude session. Exports include an interactive HTML graph, Obsidian vault, and Neo4j LOAD CSV.
- Extract structured relationships from markdown documentation into a queryable knowledge graph
- Convert unstructured technical documents into RDFS ontology for semantic search
- Build entity and relationship maps from scattered project documentation automatically
Converts unstructured documentation into queryable knowledge graphs, enabling teams to surface hidden relationships, enforce consistency, and automate downstream compliance or architecture reviews without manual curation.
Technical documentation teams and knowledge managers standardizing ontologies across large codebases or regulatory documents.
https://github.com/SenolIsci/mykg
By SenolIsci
How to Get It
pip install mykg && mykg init
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
After installing, paste this into Claude:
Help me extract structured relationships from markdown documentation into a queryable knowledge graph
Trust Signals Auto-scanned
Community Pulse New
No community discussions found yet. This doesn't mean the tool isn't good — it may be new or serve a niche use case.
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 39 GitHub stars; contributors unknown; last commit 0d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
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Evaluation
Scored from trust signals (evidence-eval-v1): 39 GitHub stars; contributors unknown; last commit 0d ago; license MIT.