From Notes to Knowledge Graph: Turning Markdown Into a Graph
If you use Obsidian, Roam, or Logseq, you already maintain an implicit graph: every wikilink is an edge, every tag is a node label. The pretty visualization those apps ship is read-only. This tutorial walks through promoting your Markdown vault into a proper graph database where you can run real queries.
Step 1: Understand what is already a graph in your notes
Your vault already encodes three kinds of edges. Wikilinks are the most explicit. Tags create implicit 'tagged-with' edges. Block references create finer-grained pointers.
Step 2: Decide your node and edge types
The mistake everyone makes here is treating every note as the same type of node. Your vault probably has at least four kinds: people, projects, concepts, and daily logs.
Step 3: Resolve aliases and unlinked mentions
Build an alias index from YAML frontmatter, match every wikilink against title+aliases, then optionally do a second pass for unlinked mentions.
Step 4: Load into a graph database
Once you have parsed and resolved everything, load it into FalkorDB or Neo4j. Use a single `UNWIND` per type for efficiency on large vaults.
Step 5: Run queries you could not run before
Find orphan notes, two notes that share many tags but never link, or PageRank-style hubs. These are queries Obsidian's graph view literally cannot answer.
Common pitfalls
- Forgetting case sensitivity. `[[Foo]]` and `[[foo]]` are the same note in Obsidian by default but different keys in your graph DB unless you normalize.
- Treating every wikilink as `LINKS_TO`. Quoting a note inside a code block creates a wikilink that should not become an edge.
- Not preserving link context. Storing the surrounding sentence on the edge as a `context` property is what lets you reconstruct why a link exists.
- Skipping daily-note inflation. If your vault has 1000+ daily notes, they will dominate degree centrality.
- Hardcoding paths. Always derive note IDs from filename stems, not absolute paths.
Related reading
Frequently Asked Questions
Will this break my Obsidian setup?
No — the parser only reads the Markdown files, it never writes to them. Your vault stays intact. The graph database is a parallel index you can rebuild from scratch any time.
How big can the vault be?
We have tested this approach on 25,000-note vaults loading into FalkorDB on a 4-core VPS in under 90 seconds.
Can I keep the graph in sync as I edit notes?
Yes. Watch the vault directory with `watchdog` (Python) or `chokidar` (Node) and re-parse only the changed files. End-to-end latency is typically under 1 second per save.
What about Roam-style block references?
Treat blocks as first-class nodes. A block reference becomes an edge between two block nodes. Roam's outliner makes this much more important than in Obsidian.
Is this overkill for personal use?
If your vault has under 500 notes, probably yes. Past ~2000 notes, the built-in view becomes a hairball and the queryable graph starts paying for itself within a week.
How do I handle backlinks that have not been written yet?
Use Obsidian's 'Unlinked Mentions' panel as inspiration: at parse time, search note bodies for any known title, surface them as candidate edges, and let the human accept or reject. Storing them as `(:Note)-[:CANDIDATE_LINKS_TO]->(:Note)` keeps proposed links separate from confirmed ones until you triage.
Should I version the graph database alongside my vault?
No — keep the Markdown files as the source of truth in git, and treat the graph database as a derived artefact you can rebuild from scratch in seconds. Storing the graph itself in version control adds noise without gaining anything.
Source
Niklas Luhmann's Zettelkasten contained ~90,000 hand-written index cards with explicit cross-references and is the direct ancestor of Obsidian, Roam, and Logseq. Luhmann published 70+ books and 400+ articles using this system, demonstrating that a sufficiently dense link graph turns notes into a 'communication partner'. [link]
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