Full definition
Semantic Search Optimization is the practice of optimizing content for search engines that match by meaning rather than by keyword — engines that encode both the query and the document as dense vector embeddings and rank by semantic similarity. Semantic search underlies modern Google (BERT, MUM), Bing, every vector database, and the retrieval layer of every AI answer engine. The shift from lexical to semantic retrieval has three optimization consequences. First, keyword density is dead — the model does not count occurrences, it measures semantic coverage, which means the content must cover the full topical surface area of the category rather than repeating the target keyword. Second, entity coverage matters more than keyword coverage — the content must mention all the related entities (people, places, concepts, products) that the model expects to find in a comprehensive treatment of the topic. Third, topical authority is built through cluster publishing — a hub page supported by 10–20 spoke pages on sub-topics, all interlinked, produces a stronger semantic signal than a single mega-page. Harch Atelier uses GLM-4 to generate embedding-aware content clusters where the cosine similarity between the priority pages and the high-intent commercial queries is maximized by design, then monitors semantic coverage weekly against the top 50 commercial queries in the client's category.