融合主题相似度与合著网络的学者标签扩展方法研究

Expanding Scholar Labels with Research Similarity and Co-authorship Network

【目的】对学者未来的研究方向和研究兴趣进行预测,设计从文献摘要中抽取并扩展学者学术标签的方法。【方法】通过TF-IDF方法从摘要中抽取基础学术标签,结合学者间主题相似度与学者间的合作关系,使用相似学者和团队内其他学者的标签对基础学术标签进行扩展。【结果】与直接使用学者当前学术标签预测学者未来学术标签相比,融合主题相似度与合著网络对学术标签进行扩展后预测召回率平均提高8.33%。【局限】实证样本小,方法只针对单一语种论文,没有涵盖学者发表的其他语种论文,方法普适性仍需要进一步确认。【结论】本文提出的方法对学者未来研究方向和研究兴趣有一定的预测能力。

[Objective]This paper tries to add more academic labels for researchers from scholarly abstracts,aiming to predict their future research interests.[Methods]First,we extracted the basic labels from abstracts with the TF-IDF method.Then,we identified researchers sharing similar academic interests and co-authoriship.Finally,we expanded the basic labels with those from similar scholars and team members.[Results]Compared with existing methods,the proposed one increased recall rate of predicting by 8.33%on average.[Limitations]Our sample size was small,and we only examined scholarly articles in one language.[Conclusions]The proposed method could predict scholars’future research interests.