The necessity for scholarly knowledge mining and management has grown significantly as academic literature and its linkages to authors produce enormously. Information extraction, ontology matching, and accessing academic components with relations have become more critical than ever. Therefore, with the advancement of scientific literature, scholarly knowledge graphs have become critical to various applications where semantics can impart meanings to concepts. The objective of study is to report a literature review regarding knowledge graph construction, refinement and utilization in scholarly domain. Based on scholarly literature, the study presents a complete assessment of current state-of-the-art techniques. We presented an analytical methodology to investigate the existing status of scholarly knowledge graphs (SKG) by structuring scholarly communication. This review paper investigates the field of applying machine learning, rule-based learning, and natural language processing tools and approaches to construct SKG. It further presents the review of knowledge graph utilization and refinement to provide a view of current research efforts. In addition, we offer existing applications and challenges across the board in construction, refinement and utilization collectively. This research will help to identify frontier trends of SKG which will motivate future researchers to carry forward their work.

随着学术文献及其与作者的联系大量产生，学术知识挖掘和管理的必要性显着增加。信息提取、本体匹配和访问具有关系的学术组件变得比以往任何时候都更加重要。因此，随着科学文献的进步，学术知识图对于语义可以赋予概念意义的各种应用变得至关重要。研究的目的是报告有关学术领域知识图谱构建、细化和利用的文献综述。该研究以学术文献为基础，对当前最先进的技术进行了全面评估。我们提出了一种分析方法来调查学术知识图谱的现状(SKG) 通过构建学术交流。这篇评论文章调查了应用机器学习、基于规则的学习和自然语言处理工具和方法构建 SKG 的领域。它进一步介绍了对知识图的利用和细化的回顾，以提供对当前研究工作的看法。此外，我们在建设、改进和利用方面全面提供现有应用和挑战。这项研究将有助于确定 SKG 的前沿趋势，这将激励未来的研究人员继续他们的工作。