What is semantic SEO?
Semantic SEO is the process of optimizing your content for a topic rather than a single keyword or phrase. It looks into user intent, user experience, and the relationships between related entities and concepts. This approach helps search engines deliver more meaningful results and enhances the overall user experience in search.
Semantic SEO and entities
Since search engines are not human and don’t have an emotional connection to concepts and ideas like us, Google had to take, a mathematical approach to “explain the world” to them. This resulted in Google’s User-context-based search engine patent.
This system is designed to identify informational context by analyzing words, phrases, and their combinations. It divides information into distinct topics (domains) and identifies unique words or phrases that help classify the content. It creates a vocabulary list, where each word has a “context vector” based on its appearance rate in each domain.
When a topic belongs to a unique domain, Google can use its knowledge base to understand the topic’s meaning. It looks for related terms from that domain to determine the page’s topical context.
Also, note the importance of entities in this context, which are individuals, places, organizations, concepts, or any distinct object or idea that holds meaning.
Search engines use entities to better understand the relationships between different concepts and the contexts they appear in. Entities allow search engines to transcend literal keyword matching and grasp the intent behind a search query. For example, if a user enters a request containing “apple,” search engines can figure out whether they mean the fruit or the company by looking at the context and other related entities in the query.
This is possible thanks to embeddings, which help computers understand both the meaning of words (semantic) and how they are used in sentences (syntax). Embeddings transform words or phrases into numbers (vectors) by placing similar words closer together in a virtual space. For instance, in this vector space, words like “king” and “queen” appear near each other because they are strongly related. Instead of searching through all the data, these vector databases compare numerical representations (vectors) to find the closest match.