计算机科学 ›› 2022, Vol. 49 ›› Issue (9): 64-69.doi: 10.11896/jsjkx.220500196
宋杰, 梁美玉, 薛哲, 杜军平, 寇菲菲
SONG Jie, LIANG Mei-yu, XUE Zhe, DU Jun-ping, KOU Fei-fei
摘要： 科技论文数据的知识表征是一个有待解决的问题,而如何学习科技论文异质网络中论文节点的表示是解决这一问题的核心。文中提出了一种基于无监督集群级的科技论文异质图节点表示学习方法(Unsupervised Cluster-level Scientific Paper Heterogeneous Graph Node Representation Learning Method,UCHL),以获取科技论文异质图中节点(作者、机构与论文等)的表示。基于科技论文异质图表示对整个异质图进行链接预测,获取节点之间边的关系,即论文与论文之间的关联关系。实验结果表明,在真实的科技论文数据集上,所提方法在多项评测指标上都取得了更优的性能。
中图分类号:
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