Semantic search attempts to apply the user intent and meaning (semantics) of the words and phrases it uses to find the most appropriate content. In this case, it is about going beyond keyword matching, as it uses information that may not be immediately present in the text, but is closely linked to what the searcher wants.
Semantic search allows Google’s new AI algorithms to “predict” user searches and understand a query within its geographic or subject context.
In this way, semantic search applies the user’s intent, the context and the conceptual meanings to match a user query with corresponding content. To do this, vector search and machine learning are used to return results that are intended to match the user’s query, even when there are no word matches. These components work to find and rank results based on their meaning, with context being one of the most important pieces.
The context in which a search occurs is important to understanding what a searcher is trying to find. The context can be as simple as the geographic space. In this sense, an intelligent search engine will use context at the search level. This means that the result for the search “soccer match” will be different for a user in Spain and for a user in the USA, since the word has different meanings in the two regions.
Thus, at the personalization level, semantic search takes information about tastes, previous searches, and previous interactions on the search engine to provide a list of results that will be tailored to what the user is looking for.
On the other hand, at a more general level, a search engine can re-rank results using information about how all searchers interact with search results, such as the results that receive the most clicks or even based on the seasonality of the most popular results.
Semantic search can also leverage context within text, using synonyms to enhance keyword search, thereby broadening query matches to related content.
On the other hand, search engines must also take into account the user’s intent when performing a search. Search engines must be able to anticipate search intent to find the really important results and eliminate those that are not suitable, even if they match textually.
how does semantic search differ from keyword search?
Although in both cases, search engines use natural language processing to improve the word matching semantic search is able to provide results where there is no matching text, something that keyword search cannot do.
In short, the big difference lies in how the matching between the query and the records occurs. While keyword search occurs by matching in the text, in semantics, the search is much broader, using concepts and being able to provide results in multiple languages.
In other words, semantic search provides greater intelligence to match on concepts rather than words, by using vector search. With this intelligence, semantic search can act more human-like.
