A Survey on Extracting Knowledge Graphs by Employing Natural Language Processing

We live in a world of information, and with the ever-increasing rate of content growth, we have no choice but to use machine-based solutions to manage, classify, and use it. However, the produced content is often unstructured, and for computer systems to be able to read and process it, there is no choice but to convert it to structured text. Knowledge graph is one of the ways to represent data in a structured way, and its roots are found in the semantic web. There are different approaches to creating a knowledge graph, but given the significant advances in artificial intelligence, the use of natural language processing is one of the most accurate and automatic ways to do this. In this paper, an attempt has been made to review the latest methods proposed for creating a knowledge graph based on natural language processing. This first allows us to have a better understanding of this research area; in addition, the approaches used by researchers in this field are reviewed and the pros and cons of these approaches are identified. Also, the existing research areas for further work and even the initial ideas for new creative solutions are provided for researchers.