Knowledge Representation for Intelligent Applications

This course explores the theory and practice of building knowledge graphs from unstructured text, equipping students to develop AI-powered systems that can reason with structured information. The course progresses from foundational concepts in knowledge representation and language models to advanced techniques in entity extraction, knowledge graph construction, and semantic web technologies. Students will learn to enhance applications with graph-based retrieval augmented generation (GraphRAG), create effective visualizations, and fine-tune models for domain-specific knowledge needs. Designed to be accessible for those with minimal coding experience, the curriculum emphasizes hands-on learning through Python labs, guided projects with real-world datasets, and a culminating final project that demonstrates practical application. Students will develop a critical understanding of interpretability and ethical considerations in knowledge-intensive AI systems.