Building Knowledge Graphs - LLM Enhanced Approach for Educational Video Recommendations

Overview

Discover how to build intelligent knowledge graphs for educational video recommendations by combining LLMs with semantic embeddings to extract concepts, map relationships, and generate personalized learning paths.

Syllabus

• Introduction to Knowledge Graphs
Overview and definition of knowledge graphs
Applications in education and personalized learning
Key components: nodes, edges, and properties
• Large Language Models (LLMs) in Education
Introduction to LLMs and their capabilities
Role of LLMs in enhancing educational technologies
Brief overview of popular LLMs and frameworks
• Semantic Embeddings for Concept Extraction
Introduction to semantic embeddings
Techniques for concept extraction using embeddings
Mapping semantic embeddings to knowledge graph nodes
• Building the Knowledge Graph
Data collection and pre-processing for educational content
Identifying entities and relationships in video metadata
Structuring and implementing the knowledge graph
• Integrating LLMs with Knowledge Graphs
Methods for combining LLM outputs with knowledge graphs
Enhancing entity recognition and relationship mapping
Utilizing LLMs for contextual understanding
• Educational Video Recommendations
Developing recommendation algorithms using knowledge graphs
Techniques for generating personalized learning paths
Case studies and examples of successful implementations
• Evaluation Metrics and Continuous Improvement
Assessing the effectiveness of video recommendations
Feedback loops for improving the knowledge graph
Future trends and opportunities in AI-enhanced education
• Hands-On Project
Building a simple knowledge graph for educational videos
Applying LLM-enhanced methods for recommendations
Presenting project outcomes and insights
• Conclusion and Future Directions
Recap of key learnings
Discussion on the future of AI in educational technologies
Q&A and open discussion session for innovative ideas

Subjects

Computer Science