中文文献题录数据作者重名消解问题研究

Study on Author Name Disambiguation for Chinese Bibliographic Information

在对目前各种作者重名消解方法进行总结的基础上,针对中文文献题录数据特征,将重名消解问题转换为同名作者文献的分类问题,提出一种基于规则和相似度的重名消解框架模型,并对其中的分解规则和合并规则进行详细的算法描述,最后选取3个学科的重名作者数据集进行实验,实验结果表明该模型能有效提高作者重名消解的准确率。

In view of the characteristics of the Chinese bibliographic information, this paper summarized the current name disambiguation methods, proposed a model based on rules and the similarity, and thus converted the author name disambiguation problems to the literature classification problems. Specific decomposition and merger rules and corresponding algorithm had been described in detail. Finally some same - named bibliographic information of the three disciplines were selected as the experimental data sets, the results showed that this model can effectively improve the correct of name disambiguation.