科研项目同行评议专家学术专长匹配方法

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...

Matching method for academic expertise of research project peer review experts

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 multi-granularity 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.