Entities

Entities are real-world things or concepts—like people, places, and ideas—not just keywords or names that are used to describe them. Think of entities as the building blocks of meaning that help artificial intelligence systems understand context and relationships in the same way humans naturally do.

A core tenet of semantic search, entities are used by large language models to understand the complex relationships between words, concepts, and real-world objects. By tapping into vast knowledge graphs and entity-rich structured data sets, LLMs can significantly reduce hallucinations and increase accuracy when processing and generating content. This entity-based approach allows AI systems to move beyond simple keyword matching to truly comprehend the semantic meaning and context of information.

For marketers interested in AI search optimization, strategically managing the data that is associated with your brand entity can dramatically improve search results and AI-generated content accuracy. Address the entity relationships of your brand through comprehensive schema markup implementation, knowledge graph optimizations, and semantic content optimizations that clearly define your brand's attributes, relationships, and context.

This entity-focused approach ensures that AI systems have access to accurate, structured information about your brand, leading to better representation in AI-generated responses, improved search visibility, and more precise targeting of relevant audiences across digital platforms.