Build Knowledge Graphs from Unstructured Text Using AI

🧠 Knowledge Graphs are becoming a critical component of modern AI systems, enabling structured reasoning, semantic search, advanced RAG architectures, and graph-based intelligence. In this tutorial, you'll learn how to build an end-to-end Knowledge Graph Pipeline using the kg-gen library to automatically transform unstructured text from documents, conversations, and other sources into a rich, queryable graph structure. We'll cover entity extraction, relationship mapping, clustering, graph analytics, visualization, and exporting data into industry-standard formats. 📌 What You'll Learn ✅ Introduction to Knowledge Graphs ✅ Converting unstructured text into structured graph data ✅ Extracting entities and relationships automatically ✅ Building automated knowledge graph pipelines ✅ Entity clustering and redundancy resolution ✅ Graph analytics with NetworkX ✅ Calculating PageRank and node importance ✅ Community detection and graph segmentation ✅ Interactive graph visualization using PyVis ✅ Querying graph relationships and insights ✅ Exporting graphs to JSON and GraphML ✅ Building graph-grounded intelligence systems 🚀 End-to-End Workflow 1️⃣ Ingest Documents & Conversations 2️⃣ Extract Entities & Relationships 3️⃣ Build Knowledge Graph Structure 4️⃣ Cluster Similar Concepts 5️⃣ Analyze Graph Importance Metrics 6️⃣ Detect Communities & Patterns 7️⃣ Visualize Graph Interactively 8️⃣ Query Relationships & Insights 9️⃣ Export for Integration with Other Systems