A Survey on Extraction of Relations using Knowledge Graphs in Various Applications

Over the past ten years, knowledge graphs (KG) have rapidly become a significant field. A growing number of studies are focusing on knowledge graphs to structurally represent various relationships that exist between the entities. Knowledge graphs have quickly become a significant area in AI. A directed, labelled, multi-relational graph with some type of semantics is what is meant by the definition of a knowledge graph, which continues a longstanding tradition of graphs in the AI community. To provide such a synthesis, this paper presents a review of the KG research landscape first demonstrates what the main research areas are, and how they relate to various communities, such as in question-answering, error-recognition, and recommendation systems. Within these communities, the many but related focus of KG research are presented in a unified framework. Deep learning techniques are used by the majority of cutting-edge systems, primarily to target relationships between entities.