The domain classification of scientific knowledge objects has been continuously improved over the years. Systems that can automatically classify a scientific knowledge object, through the use of artificial intelligence, machine learning algorithms, natural language processing, and others, have been adopted in most scientific knowledge databases to maintain internal classification consistency as well as to simplify the information arrangement. However, the amount of available data has grown exponentially in the last few years and now it can be found in multiple platforms under different classifications due to the implementation of different classification systems. Thus, the process of searching and selecting relevant data in research studies and projects has become more complex and the time needed to find the right information has continuously grown as well. Therefore, machine learning and natural language processing play an important role in the development and achievement of automatic and standardized classification systems that will aid researchers in their research work.

The growth of scientific production, associated with the increase in the complexity of scientific contents, makes the classification of these contents highly subjective and subject to misinterpretation. The taxonomy on which this classification process is based does not follow the scientific areas' changes. These classification processes are manually carried out and are therefore subject to misclassification. A classification process that allows automation and implements intelligent algorithms based on Machine Learning algorithms presents a possible solution to subjectivity in classification. Although it does not solve the inadequacy of taxonomy, this work shows this possibility by developing a solution to this problem. In conclusion, this work proposes a solution to classify scientific content based on the title, abstract, and keywords through Natural Language Processing techniques and Machine Learning algorithms to organize scientific content in scientific domains.