一种基于异质信息网络的学术文献作者重名消歧方法

Author Name Disambiguation Based on Heterogeneous Information Network

【目的】充分利用学术文献中的实体关系数据解决作者重名消歧问题。【方法】从文献信息中抽取多种类型节点及其关系构建异质信息网络,采用网络表示学习方法获取作者节点的表示向量并利用聚类分析得到初步划分,最后基于强规则匹配融合多个聚类簇得到消歧结果。【结果】在构建的Web of Science数据集下进行测试,本文方法的K-Metric平均值达0.842,较对比方法提升了63.18%,即使不考虑强规则匹配依然提升了34.69%。【局限】该方法需要利用引文信息,应用场景具有一定的局限性。【结论】基于异质信息网络,利用更丰富的实体关系对作者节点进行表示学习,能有效改善作者重名消歧的效果。

[Objective]The paper tries to improve author name disambiguation with entity relationship data from academic literature.[Methods]First,we extracted multi-type nodes and their relationships from literature to construct a heterogeneous information network(HIN).Then,we applied representation learning to obtain the latent vectors of authors,and used clutering analysis to get a preliminary division.Finally,we merged several clusters based on strong rule matching to obtain the disambiguation.[Results]We examined the new model with dataset from the Web of Science.The K-Metric mean value was 0.842,a 63.18%increase over the baseline model.Without strong rule matching,the improvement also reached 34.69%.[Limitations]The proposed model requires citation information,which limited its application scenarios.[Conclusions]Our new method could effectively improve the performance of author name disambiguation.