Comparative Review of Tasks, Approaches and Tools for Automated Knowledge Extraction from the Texts of Scientific Publications

Abstract

The purpose of this work is to review the existing technologies for automated knowledge extraction from scientific publications. The main tasks include an analysis of existing methods for automated knowledge extraction, as well as an overview of various software tools used to solve this problem. The article presents a description of the main approaches to automated knowledge extraction, such as machine learning, natural language processing and the development of various methodologies for building knowledge graphs. An analysis of existing sources showed that the main problems associated with automated knowledge extraction are the need to create a large amount of labeled data, the processing of complex structured data, and the need to develop new algorithms for working with such data.