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How to Build Knowledge Graph Generation Pipelines From Text With kg-gen, NetworkX Analytics, and Interactive Visualizations

This tutorial walks through building a complete knowledge graph generation pipeline from plain text, conversations, and multiple documents using kg-gen. It covers entity extraction, chunking, clustering, aggregation, NetworkX analytics (centrality, PageRank, community detection), interactive visualization with PyVis, and export to JSON/GraphML.

SourceMarkTechPostAuthor: Sana Hassan
How to Build Knowledge Graph Generation Pipelines From Text With kg-gen, NetworkX Analytics, and Interactive Visualizations
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Key points

  • Learn to generate knowledge graphs from text using kg-gen and LiteLLM.
  • Explore chunking, clustering, and aggregation of multiple sources.
  • Analyze graphs with NetworkX centrality, PageRank, and community detection.
  • Visualize and export graphs as JSON, GraphML, and interactive HTML.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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