基于相似特征和关系图优化的姓名消歧

Name Disambiguation Based on Similar Features and Relation Graph Optimization

【目的】充分利用学术文献的特征信息和关系信息解决作者姓名消歧问题。【方法】提出了一种特征信息嵌入和关系图优化相结合的姓名消歧方法。首先基于文本信息提取文献特征,通过表示学习得到文献的嵌入向量,然后挖掘文献之间的关系信息并分析关系强弱,构建4个关系图以优化每篇文献嵌入向量,最后使用凝聚层次聚类算法得到消歧结果。【结果】在AMiner-na数据集上的实验结果表明,本文方法得到的F1分数平均值为68.78%,相比次优方法提升了1.81个百分点。【局限】注重所有作者的平均消歧效果,部分作者消歧效果有待提高。【结论】本文方法能够充分利用文献关系信息,综合特征信息有效地提升作者姓名消歧的效果。

[Objective]The paper aims to fully utilize the feature information and relation information of academic literature to improve author name disambiguation.[Methods]We proposed a name disambiguation method combining feature information embedding and relation graph optimization.First,we extracted feature information from literature and applied representation learning to obtain the embedding vectors.Then,we mined the relationship information between literatures,and also constructed four relation graphs to optimize the embedding vectors of each literature.Finally,we used hierarchical agglomerative clustering algorithm to obtain the disambiguation results.[Results]We examined the new model on AMiner-na dataset and found its average F1 score reached 68.78%,which was 1.81 percent points higher than the second best method.[Limitations]The proposed method focuses on the average disambiguation effect of all authors,and the disambiguation effect of some authors needs to be improved.[Conclusions]The proposed method can fully utilize the literature relation information,and effectively improve the effect of author name disambiguation.