Automatic processing of bibliographic data becomes very important in digital
libraries, data science and machine learning due to its importance in keeping
pace with the significant increase of published papers every year from one side
and to the inherent challenges from the other side. This processing has several
aspects including but not limited to I) Automatic extraction of references from
PDF documents, II) Building an accurate citation graph, III) Author name
disambiguation, etc. Bibliographic data is heterogeneous by nature and occurs
in both structured (e.g. citation graph) and unstructured (e.g. publications)
formats. Therefore, it requires data science and machine learning techniques to
be processed and analysed. Here we introduce BiblioDAP'21: The 1st Workshop on
Bibliographic Data Analysis and Processing.

介绍了 BiblioDAP'21: 第一届 Bibliographic Data Analysis and Processing 工作坊，指出由于发表的论文数量的显著增加以及固有的挑战，自动处理引文数据在数字图书馆、数据科学和机器学习中变得非常重要，其包括自动从 PDF 文档中提取引用、构建精确的引文图和作者名称消歧等方面。