Information to Inference –A Process Flow using Knowledge Graph

Abstract

Every day a massive amount of data is created due to the constant use of the internet. During the COVID-19 pandemic, people are constantly learning to obtain knowledge at a faster pace. The knowledge graph can represent huge volumes of data and inference can be gained in lesser time. In our work, we have proposed a technique that initially cleans the scholarly data and then retrieves the entities and explores the relations between entities. Finally, we can split the knowledge graph using different methods to gain knowledge about the nodes and find the reasoning in their relation. The experimental results visually indicate that we can draw inference from the real triplets of the customized knowledge graph.