Purpose

The COVID-19 pandemic has sparked a remarkable volume of research literature, and scientists are increasingly in need of intelligent tools to cut through the noise and uncover relevant research directions. As a response, the authors propose a novel framework. In this framework, the authors develop a novel weighted semantic graph model to compress the research studies efficiently. Also, the authors present two analyses on this graph to propose alternative ways to uncover additional aspects of COVID-19 research.

Design/methodology/approach

The authors construct the semantic graph using state-of-the-art natural language processing (NLP) techniques on COVID-19 publication texts (>100,000 texts). Next, the authors conduct an evolutionary analysis to capture the changes in COVID-19 research across time. Finally, the authors apply a link prediction study to detect novel COVID-19 research directions that are so far undiscovered.

Findings

Findings reveal the success of the semantic graph in capturing scientific knowledge and its evolution. Meanwhile, the prediction experiments provide 79% accuracy on returning intelligible links, showing the reliability of the methods for predicting novel connections that could help scientists discover potential new directions.

Originality/value

To the authors’ knowledge, this is the first study to propose a holistic framework that includes encoding the scientific knowledge in a semantic graph, demonstrates an evolutionary examination of past and ongoing research and offers scientists with tools to generate new hypotheses and research directions through predictive modeling and deep machine learning techniques.

中文翻译：

---

未知的知识：一种基于图的时间 COVID-19 文献挖掘方法

目的

COVID-19 大流行引发了大量研究文献，科学家们越来越需要智能工具来消除噪音并发现相关研究方向。作为回应，作者提出了一个新颖的框架。在这个框架中，作者开发了一种新颖的加权语义图模型来有效地压缩研究。此外，作者对该图进行了两项分析，以提出替代方法来揭示 COVID-19 研究的其他方面。

设计/方法/方法

作者使用最先进的自然语言处理 (NLP) 技术对 COVID-19 出版物文本（> 100,000 文本）构建语义图。接下来，作者进行了进化分析，以捕捉 COVID-19 研究随时间的变化。最后，作者应用链接预测研究来检测迄今为止尚未发现的新型 COVID-19 研究方向。

调查结果

研究结果揭示了语义图在捕获科学知识及其演变方面的成功。同时，预测实验在返回可理解链接方面提供了 79% 的准确率，显示了预测新连接方法的可靠性，可以帮助科学家发现潜在的新方向。

原创性/价值

据作者所知，这是第一项提出整体框架的研究，该框架包括在语义图中编码科学知识，展示对过去和正在进行的研究的进化检验，并为科学家提供工具，通过预测生成新的假设和研究方向。建模和深度机器学习技术。