Semantic Exploring and Analysis on Visualization of Research Articles Based on Knowledge Graphs

Knowledge graphs, which indicate knowledge as a semantic graph, have stirred up notable worries in the professional as well as academic communities. Many researchers believe that their ability to provide semantically structured information holds great promise for the development of more intelligent machines. This trait has important potential solutions for many tasks including information retrieval recommendations, data visualization, and question-answering. The use of knowledge graphs to depict the structural relationships between items is becoming increasingly common. In the last few years, there has been a remarkably rapid expansion in the application of knowledge representation, visualization, and reasoning in diverse fields like natural language processing and computer vision. This has resulted in the production of numerous open-source and enterprise-supported knowledge graphs. The tools used for generating knowledge graphs also create hype in the market as these tools can generate and analyze knowledge graphs within minutes. This paper compares knowledge graph generation tools based on various factors, including how they were created and analyzed, and the outcomes obtained after visualization, like clustering and centrality based on the number of connections, betweenness centrality, closeness centrality, and PageRank.