List the actual questions you ask: What supports this argument? Who authored this dataset? Which concept supersedes another? Encode only those relationships you regularly seek. For each relation, define direction, allowed entities, and examples. Then test on a small corpus before scaling. Practical relevance keeps your graph lean, fast to maintain, and genuinely helpful in moments when time is short and stakes feel high.
Prefer human-friendly structures like YAML front matter, simple link types, or JSON-LD snippets for interoperability. Document permitted values and constraints in a reference note. Tools change, but readable metadata ages gracefully. Keeping the schema approachable encourages consistent capture, even during rushed research sessions. Your future self will thank you when bulk edits, cross-tool exports, and integrity checks feel obvious rather than intimidating technical undertakings requiring fragile scripts.
Choose one focused project and model ten to twenty notes with proposed entities and relationships. Run queries that reflect real decisions you need to make next week. If results feel immediately useful, continue; if not, simplify ruthlessly. Early prototyping prevents sprawling complexity and protects enthusiasm. The best ontologies earn their keep quickly, offering navigational superpowers without demanding a new career in data modeling or endless refactoring.
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