Introduction to Knowledge Graph Embeddings and Factorization Methods

Knowledge graphs organize real-world information into interconnected networks, but computers need a structured, mathematical way to process these relationships. Factorization-based graph embeddings solve this by translating complex entities and links into dense vector spaces. In this course, you will learn the foundational concepts behind knowledge graph embeddings and explore the key tensor factorization algorithms used to represent relational data. By reading the detailed explanations and working through written exercises, you will understand how to model structured knowledge for downstream machine learning tasks, including link prediction and semantic search. What you'll learn: 1. Understand the fundamental terminology of knowledge graphs, triples, and semantic relations. 2. Explore how factorization models map entities and relations into continuous vector spaces. 3. Analyze the mathematical structures of RESCAL, DistMult, and HolE algorithms. 4. Compare the strengths and limitations of symmetric versus asymmetric relation modeling. 5. Evaluate embedding quality using standard metrics like Mean Reciprocal Rank (MRR) and Hits@K. 6. Discover how knowledge graph embeddings integrate with modern vector databases and retrieval-augmented generation systems. The course begins with foundational definitions of knowledge graphs before guiding you step-by-step through tensor decomposition math and modern evaluation strategies. You will progress from core theory to understanding practical application architectures in today's AI landscape. This course is designed for beginners in data science, machine learning, or software engineering who want to understand graph representation learning, with no prior experience with graph embeddings required. Start reading today to master the mathematical foundations of structured knowledge representation.