谁？基于书目数据的作者姓名深度消歧
Whois? Deep Author Name Disambiguation using Bibliographic Data
摘要：
随着作者的数量逐年呈指数级增长，同名作者的数量也在成比例地增加。这使得将新发表的论文分配给适当的作者变得具有挑战性。因此，作者姓名歧义（ANA）被认为是数字图书馆中的一个关键的开放性问题。本文提出了一种作者姓名消歧（AND）方法，该方法通过利用他们的合著者和研究领域将作者姓名与他们的真实世界实体联系起来。为此，我们使用DBLP存储库中的一个集合，其中包含由大约260万名合著者撰写的500多万条书目记录。我们的方法首先将具有相同姓氏和相同名字首字母的作者分组。通过捕获与其共同作者和研究领域的关系来识别每组中的作者，该关系由相应作者的经验证的出版物的标题表示。为此，我们训练一个神经网络模型，该模型从合著者和标题的表示中学习。我们通过在大型数据集上进行广泛的实验来验证我们方法的有效性。
英文摘要：
As the number of authors is increasing exponentially over years, the number of authors sharing the same names is increasing proportionally. This makes it challenging to assign newly published papers to their adequate authors. Therefore, Author Name Ambiguity (ANA) is considered a critical open problem in digital libraries. This paper proposes an Author Name Disambiguation (AND) approach that links author names to their real-world entities by leveraging their co-authors and domain of research. To this end, we use a collection from the DBLP repository that contains more than 5 million bibliographic records authored by around 2.6 million co-authors. Our approach first groups authors who share the same last names and same first name initials. The author within each group is identified by capturing the relation with his/her co-authors and area of research, which is represented by the titles of the validated publications of the corresponding author. To this end, we train a neural network model that learns from the representations of the co-authors and titles. We validated the effectiveness of our approach by conducting extensive experiments on a large dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
