科研项目同行评议专家学术专长匹配方法

Matching method for academic expertise of research project peer review experts

现有的评审专家推荐过程通常依赖于人工匹配,在进行专家推荐时不能充分捕捉评审项目所属学科与专家研究兴趣之间的语义关联,导致专家推荐的精确性较低。为解决这个问题,提出了一种科研项目同行评议专家学术专长匹配方法。该方法构建学术网络以建立学术实体联系,并设计元路径捕捉学术网络中不同节点间的语义关联;使用随机游走策略获得项目所属学科与专家研究兴趣共现关联的节点序列,并通过网络表示学习模型训练得到具有语义关联的项目所属学科与专家研究兴趣的向量表示;在此基础上,按照项目学科树层次结构逐层计算语义相似度,以实现多粒度的同行评议学术专长匹配。在爬取的知网和万方论文数据集、某专家评审数据集、以及百度百科词向量数据集上得到的实验结果表明,所提方法能提升项目所属学科与专家研究兴趣间的语义关联,并能有效应用于项目评审专家的学术专长匹配。

Most of the existing expert recommendation processes rely on manual matching,which leads to the low accuracy of expert recommendation due to that they cannot fully capture the semantic association between the subject of the project and the interests of experts.To solve this problem,a matching method for academic expertise of project peer review experts was proposed.In the method,an academic network was constructed to establish the academic entity connection,and a meta-path was designed to capture the semantic association between different nodes in the academic network.By using the random walk strategy,the node sequence of co-occurrence association between the subject of the project and the expert research interests was obtained.And through the network representation learning model training,the vector representation with semantic association of the project subject and expert research interests was obtained.On this basis,the semantic similarity was calculated layer by layer according to the hierarchical structure of project subject tree to realize multigranularity peer review academic expertise matching.Experimental results on the crawled datasets of HowNet and Wanfang papers,an expert review dataset and Baidu Baike word vector dataset show that this method can enhance the semantic association between the project subject and expert research interests,and can be effectively applied to the academic expertise matching of project review experts.