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Learn how to manually construct a knowledge graph for educational content in this 35-minute tutorial focused on building a learning path recommender system. Explore the expert-driven approach to defining nodes (topics) and edges (relationships) for educational videos covering Python programming, financial analysis, and bioimage analysis. Discover techniques for annotating topics with metadata including difficulty levels, keywords, domain classifications, and detailed descriptions, while manually weighting relationships to indicate prerequisites, content similarity, and learning progression. Visualize your knowledge graph using both interactive Pyvis and static Matplotlib methods, and implement SQLite database storage for persistence and querying. While this manual construction method ensures high precision in educational content organization, understand its maintenance challenges for larger libraries and gain practical skills for creating meaningful learning paths based on domain expertise.
