Designing Entity Linking Systems for Text and Knowledge Bases

Unstructured text is filled with ambiguous references, making it difficult for systems to connect words to real-world concepts. Designing an entity linking system bridges this gap, transforming raw text into structured, actionable data. This text-only course guides you through the foundational concepts and system architectures required to map named entities to rich knowledge bases. You will understand how to resolve linguistic ambiguity and scale your pipeline for real-world applications. What you'll learn: Understand key concepts of Named Entity Recognition (NER) and Named Entity Disambiguation (NED); Explore knowledge base schemas and how to query structured data sources; Design candidate generation pipelines using both traditional lexical search and modern dense retrieval; Apply ranking and disambiguation algorithms to resolve naming conflicts; Integrate vector databases to optimize semantic search and system performance; Evaluate system accuracy using standard NLP metrics and validation frameworks. The course begins with core terminology and foundational definitions of entity linking. You will then progress through system architecture, candidate generation, and modern deep-learning-based disambiguation strategies. This course is designed for software engineers, data enthusiasts, and aspiring natural language processing practitioners who want to learn system design without needing advanced prerequisites. Start reading today to master the fundamentals of linking text to structured knowledge.