Semantic Decomposition

Semantic Decomposition is a type of query breakdown in AI Search by meaning and component parts of the topic. The query 'best laptop for programming' breaks down into: processor performance, RAM amount, keyboard quality, battery life, screen size — each component part (meronym) of the topic becomes a separate sub-query in the fan-out process.

This is the most commonly used type of decomposition and directly maps to lexical relations: entity + attributes + relationships from the EAV model. To cover semantic decomposition, use meronyms and hyponyms as H2 headings, because AI will break down user questions exactly along these relations. Legal industry example: the query 'how to set up a limited liability company' generates semantic sub-queries: share capital (meronym), company agreement (meronym), KRS registration (meronym), notary costs (attribute).

In practice, list all meronyms and attributes of the Central Entity using the lexical expansion tool — this gives you a ready map of semantic sub-queries to cover in the article.