Named Entity Recognition.

Named Entity Recognition (NER) is a subtask of natural language processing that locates and categorises named entities in text into predefined categories: person names, organisations, locations, dates, quantities, monetary values, and more. For example, in the sentence 'Elon Musk founded SpaceX in California in 2002', NER identifies 'Elon Musk' (Person), 'SpaceX' (Organisation), 'California' (Location), and '2002' (Date).

NER in Search Engines

Google uses NER to understand who and what a piece of content is about, building its Knowledge Graph by extracting entities and their relationships from across the web. This is how Google understands that a page about 'SEO' is also about 'search engines', 'Google', 'ranking factors', and 'digital marketing'.

SEO Applications

Explicitly mentioning relevant entities—people, brands, tools, places—in content helps search engines understand its topic and context. Structured data markup using Schema.org entity types gives NER systems clear, machine-readable entity signals that can improve knowledge panel accuracy and rich result eligibility.