RankBrain works by selecting from Google's index a database of over a billion gigabytes; but this goes further than simply comparing words, as it uses Artificial Intelligence.
Semantic search involves words not only being seen statically, but the algorithm finding their meaning, and Google had already been implementing this with Colibrí, as we mentioned previously.
However, RankBrain draws on knowledge gained from other searches to find the meaning of words.
With this algorithm, it is possible to know what the user's intention is by recording the semantics of the query, with a global meaning.
In this way, RankBrain deduces what the user is searching for through what has been learned, with previous experience, and predicts the words by solving ambiguities or unknown terms.
The idea is that this algorithm can give searchers the best search results.
On the other hand, although Google does not reveal how this algorithm manages to do all this, experts can deduce that it involves the use of vectors that allow the engines to make semantic relationships.
And this conclusion is reached because in 2013, Google launched Word2Vec, which is an open-source machine learning software that allows users to compare and measure the semantic relationships of entered words.
To do this, a multidimensional vector space is used in which each word in the linguistic corpus is represented by a vector; therefore, the more dimensions are selected, the more word relationships the software can recognize.
After this, the vectors are introduced into a neural network that, with the support of machine learning, allows to relate the words that have been placed in the same vector context.
Therefore, the similarity between these vectors is calculated by a cosine distance office email list between -1 and +1.
So, this is how the neural network achieves its learning: by providing any linguistic corpus to the software as input, the corresponding vectors will be given as output, and this allows the evaluation of the semantic closeness or distance of the words that are in said corpus.
In this way, if the software were to be faced with a new input, the learning algorithm will give it the ability to readjust the vector space and create new semantic connections or return previous results .
This is why many experts claim that RankBrain operates in a similar way, with quite similar mathematical operations.
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Beyond neuro-linguistic programming or NLP, RankBrain definitely makes use of other elements to predict the search intent of Google users, such as:
The search history.