用主题信息增强学术网络分析，揭示新冠研究中论文与学者的真实影响力。
Content Aware Analysis of Scholarly Networks: A Case Study on CORD19 Dataset
- 基于命名实体识别提取论文主题，通过改进HITS算法传播主题信息。
- 引入主题数据后，论文排名显著变化，揭示了传统引用外的深层关联。
- 适合关注疫情科研动态、知识图谱与引文分析的研究者。
本文研究科学网络中论文、研究人员与期刊之间的关系。提出一种新方法，利用基于HITS算法的主题信息传播机制，将语义信息融入学术网络。主题信息通过命名实体识别与实体链接提取，本研究采用MedCAT从CORD19数据集（涵盖新冠与冠状病毒研究的学术文章）中抽取主题。聚焦新冠领域，该方法在引文框架中整合主题信息，验证其有效性。通过混合式HITS算法的应用，结果显示引入主题数据显著影响论文排名，揭示了学术社区结构的深层模式。
原文摘要 · Abstract (English)
This paper investigates the relationships among key elements of the scientific research network, namely articles, researchers, and journals. We introduce a novel approach to use semantic information through the HITS algorithm-based propagation of topic information in the network. The topic information is derived by using the Named Entity Recognition and Entity Linkage. In our case, MedCAT is used to extract the topics from the CORD19 Dataset, which is a corpus of academic articles about COVID-19 and the coronavirus scientific network. Our approach focuses on the COVID-19 domain, utilizing the CORD-19 dataset to demonstrate the efficacy of integrating topic-related information within the citation framework. Through the application of a hybrid HITS algorithm, we show that incorporating topic data significantly influences article rankings, revealing deeper insights into the structure of the academic community.
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