Entity linking (also called entity resolution or named entity disambiguation) is the NLP task of identifying mentions of entities in text and connecting them to corresponding entries in a knowledge base (like Wikipedia, Wikidata, or a domain-specific ontology). It bridges the gap between unstructured text and structured knowledge.

How Entity Linking Works

• Step 1 — Mention Detection: Identify spans of text that refer to entities (e.g., "Apple" in "Apple released a new phone").
• Step 2 — Candidate Generation: Generate a list of possible knowledge base entries the mention could refer to (Apple Inc., apple fruit, Apple Records, etc.).
• Step 3 — Disambiguation: Use context to select the correct entity. "Apple released a new phone" → Apple Inc. vs. "I ate an apple" → the fruit.

Why Entity Linking Matters for RAG

• Grounding: Links free-text queries and documents to canonical entities, enabling structured reasoning about entities and their relationships.
• Knowledge Graph Integration: Once entities are linked, you can traverse a knowledge graph to find related entities, properties, and facts.
• Disambiguation: Resolves ambiguity — "Python" could mean the programming language, the snake, or Monty Python depending on context.
• Cross-Document Coreference: Recognizes that "TSMC," "Taiwan Semiconductor," and "the Taiwanese chipmaker" all refer to the same entity.

Modern Approaches

• Dense Retrieval: Encode mention context and entity descriptions into vectors, retrieve by similarity.
• LLM-Based: Use large language models to disambiguate in-context.
• Autoregressive: Models like GENRE generate entity names token by token conditioned on context.

Tools and Systems

• spaCy with entity linking components
• REL (Radboud Entity Linker)
• BLINK (Facebook/Meta)
• DBpedia Spotlight

Entity linking is a foundational building block for knowledge-grounded AI systems that need to reason about real-world entities.