基于特征编码和图嵌入的姓名消歧方法

针对作者姓名歧义问题,提出基于特征编码和图嵌入的作者姓名消歧方法。该方法首先利用word2vec模型对文档的属性特征进行编码从而构建文档的表征向量,然后采用图自动编码器将文档关系编码至文档向量中,聚类相似文档。为进一步提升聚类结果的准确性,使用图嵌入的方法将文档关系网络和作者关系网络的拓扑结构信息引入文档向量,进一步聚集相关文档。该方法同时利用文档的属性特征以及多个关系网络的信息,通过无监督学习的方法寻找文档表征向量,实现良好的姓名消歧效果。在真实作者数据集AMiner上的测试结果表明,该方法显著优于目前几个其他基于图网络的方法。

Name disambiguation based on encoding attributes and graph topology

Aiming at solving the problem of author name ambiguity, we propose a novel name disambiguation method based on encoding attributes and graph topology. A word2vec model is used to construct document representation vectors by encoding the attributes of documents. The relationship of documents is then encoded into the document embedding vectors by a graph auto-encoder and similar documents are aggregated. To further improve the accuracy of the clustering results, a graph embedding model is proposed to introduce the document-document network and author-author network topology into the document vectors afterword, thus related papers are moved closer. This method utilizes the information of document attributes and relationship networks at the same time, finds document representation vectors using an unsupervised model and improves the performance of name disambiguation. Experimental results on the real author dataset AMiner show that our method is superior to several state-of-the-art graph-based solutions.