Building a Knowledge Graph for Digital Communication: A Student’s Approach
Published:
Over the past semester, I’ve been building a comprehensive knowledge graph covering 14 areas of digital marketing communication. What started as a simple note-taking exercise evolved into something much more interesting: a structured, queryable map of how concepts in digital communication connect to each other.
The Framework
The knowledge base is organized around three layers:
- Chapter Notes (14 topics) — from search marketing and e-commerce to AI marketing and data privacy
- Concept Notes (19 entries) — deep dives into specific mechanisms like RFM models, recommendation systems, and private domain traffic
- Mechanism Notes — cross-cutting themes that span multiple chapters, like “Cookie deprecation → first-party data rise”
Human-AI Collaboration
One design principle I stuck to: the human perspective leads, AI supports. Each note follows a format where:
- 🙋 My questions drive the exploration
- 🤖 AI responses provide technical depth
- 📎 Cross-references link related concepts
This isn’t about letting AI write the knowledge base — it’s about using AI as a thought partner to probe deeper into concepts I’m genuinely curious about.
The Graph View
The most satisfying part is watching the Obsidian graph view grow organically over time. What began as disconnected notes gradually formed clusters, then bridges between clusters. The visual representation makes it obvious which concepts are central hubs and which are peripheral — a kind of intellectual cartography.
Tools Used
- Obsidian for note-taking and graph visualization
- Mermaid.js for inline diagrams
- Claude as an AI discussion partner
- Python for data processing and analysis
If you’re interested in knowledge management or computational approaches to learning, I’d love to connect and chat!