One of the most famous authors of the method is Tomas Mikolov. His software and method of theoretical application are the major ones for our consideration today. It is better to pay attention that it is more mathematically oriented. The use of embedding models to turn KGs into vector space has become a well-known field of research. In recent years, a plethora of embedding learning approaches have been proposed in the literature. Many of these models rely on data already stored in the input KG. Following the closed world assumption, the knowledge not presented in the KG cannot be judged untrue; instead, it may only be labeled as unknown. On the other hand, embedding models, like most machine learning algorithms, require negative instances to learn embeddings efficiently. To deal with this, a variety of negative sample generating strategies have been developed. The author himself had more to do with mathematics, and his method concerns, first of all, a mathematical solution for a theoretical, and then a practical solution for creating this and the method we are analyzing. Dense vector word representations have lately gained popularity as fixed-length features for machine learning algorithms, and Mikolov’s system is now widely used. We investigate one of its main components, Negative Sampling, and offer efficient distributed methods that allow us to scale to indicate and exclude the possibility of probability loss in a similar value. Furthermore, this method is laser-focused on a single action in the broad sense for processing the recognition of the above-mentioned vector or words. It is important to pay attention to mathematical theory and understand the importance of the neural network in this field.