Knowledge Graphs and Graph Embeddings for Beginners

In an era of highly interconnected data, traditional databases often struggle to capture the complex relationships between real-world entities. Knowledge graphs solve this by structuring information as networks of nodes and edges, enabling smarter search, recommendation, and AI reasoning. This text-only course guides you from the absolute basics of graph database concepts to understanding how knowledge graphs integrate with modern machine learning. You will learn how to represent facts, query structured knowledge, and use graph embeddings to bridge the gap between symbolic logic and vector-based AI. What you'll learn: * Understand the core terminology of knowledge graphs, including triples, ontologies, and schemas. * Query graph structures to extract precise relational data using standard conceptual patterns. * Explore graph representation learning and how embeddings translate structural relationships into dense vectors. * Discover how knowledge graphs integrate with Retrieval-Augmented Generation (RAG) to improve AI accuracy. * Analyze common use cases for graph embeddings, including link prediction and entity resolution. You will start with foundational definitions and data modeling principles before progressing to querying mechanics, embedding techniques, and modern AI integration patterns. Each section features clear written explanations and conceptual exercises to reinforce your learning. This course is designed for data enthusiasts, developers, and aspiring AI practitioners who are new to graph technologies. No prior experience with graph databases is required. Start reading today to unlock the power of structured, connected data.