Verma Shilpa, Bhatia Rajesh, Harit Sandeep, Batish Sanjay
Punjab Engineering College, Chandigarh, India.
Complex Intell Systems. 2023;9(1):1059-1095. doi: 10.1007/s40747-022-00806-6. Epub 2022 Aug 9.
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 (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关联大量产生，对学术知识挖掘和管理的需求显著增长。信息提取、本体匹配以及访问具有关系的学术组件变得比以往任何时候都更加关键。因此，随着科学文献的发展，学术知识图谱对于各种语义能够赋予概念意义的应用变得至关重要。本研究的目的是报告一篇关于学术领域知识图谱构建、完善和利用的文献综述。基于学术文献，该研究对当前的先进技术进行了全面评估。我们提出了一种分析方法，通过构建学术交流来研究学术知识图谱（SKG）的现状。这篇综述论文研究了应用机器学习、基于规则的学习以及自然语言处理工具和方法来构建SKG的领域。它进一步介绍了知识图谱利用和完善的综述，以呈现当前的研究成果。此外，我们全面提供了在构建、完善和利用方面的现有应用和挑战。这项研究将有助于识别SKG的前沿趋势，这将激励未来的研究人员推进他们的工作。
