Foundations of Structured Data and Semantic Search

Modern search has evolved far beyond basic keyword matching in plain text documents. Today, structured information such as knowledge graphs, schema markup, product profiles, and entity relationships drive how search engines interpret context and deliver precise results. This comprehensive text-based course introduces you to the core principles of structured data representation, extraction, and modern semantic search architecture. By working through clear written explanations and practical examples, you will understand how unstructured text transforms into rich, interconnected data that search systems can index and query effectively. What you'll learn: - Understand the evolution from traditional inverted index keyword search to modern entity-based retrieval - Explore common structured data standards, including Schema.org, JSON-LD, and graph models - Learn how search crawlers identify, extract, and validate structured web data - Discover the fundamentals of knowledge graphs, entity disambiguation, and relationship mapping - Examine the role of semantic vectors and modern retrieval patterns alongside structured catalogs - Apply structured markup techniques to make digital content easily discoverable by search algorithms You will begin with essential terminology and core definitions of information retrieval before progressing into structured data formats, graph models, and semantic extraction techniques. This course is designed for beginners, developers, content strategists, and data analysts with no prior search engineering experience required. Start reading today to master the foundations of structured web discovery.