fdata-02-00048-i0005_Application of a Novel Subject Classification Scheme for a Bibliographic Database Using a Data-Driven Correspondence.tif

A novel subject classification scheme should often be applied to a preclassified bibliographic database for the research evaluation task. Generally, adopting a new subject classification scheme is labor intensive and time consuming, and an effective and efficient approach is necessary. Hence, we propose an approach to apply a new subject classification scheme for a subject-classified database using a data-driven correspondence between the new and present ones. In this paper, we define a subject classification model of the bibliographic database comprising a topological space. Then, we show our approach based on this model, wherein forming a compact topological space is required for a novel subject classification scheme. To form the space, a correspondence between two subject classification schemes using a research project database is utilized as data. As a case study, we applied our approach to a practical example. It is a tool used as world proprietary benchmarking for research evaluation based on a citation database. We tried to add a novel subject classification of a research project database.在科研评价任务中，新的主题分类方案（subject classification scheme）通常需应用于已完成分类的文献书目数据库（bibliographic database）。一般而言，采用全新的主题分类方案往往耗费人力且耗时良久，因此亟需一套高效且可行的解决方案。为此，我们提出一种基于新旧主题分类方案间数据驱动对应关系的方法，以将新主题分类方案应用于已完成主题分类的数据库。 本文中，我们针对文献书目数据库定义了一种以拓扑空间（topological space）为基础的主题分类模型。随后，我们基于该模型提出了对应的解决方案，其中新主题分类方案需构建紧致拓扑空间（compact topological space）。为构建该拓扑空间，我们利用科研项目数据库（research project database）中两类主题分类方案的对应关系作为数据支撑。 作为案例研究，我们将所提方法应用于一个实际示例：该示例为一款基于引文数据库（citation database）、用于科研评价的全球专属基准测试工具，我们尝试为其中的科研项目数据库新增一套全新的主题分类方案。