The Enhanced Microsoft Academic Knowledge Graph

The Enhanced Microsoft Academic Knowledge Graph (EMAKG) is a large dataset of scientific publications and related entities, including authors, institutions, journals, conferences, and fields of study. The proposed dataset originates from the Microsoft Academic Knowledge Graph (MAKG), one of the most extensive freely available knowledge graphs of scholarly data. To build the dataset, we first assessed the limitations of the current MAKG. Then, based on these, several methods were designed to enhance data and facilitate the number of use case scenarios, particularly in mobility and network analysis. EMAKG provides two main advantages: It has improved usability, facilitating access to non-expert users It includes an increased number of types of information obtained by integrating various datasets and sources, which help expand the application domains. For instance, geographical information could help mobility and migration research. The knowledge graph completeness is improved by retrieving and merging information on publications and other entities no longer available in the latest version of MAKG. Furthermore, geographical and collaboration networks details are employed to provide data on authors as well as their annual locations and career nationalities, together with worldwide yearly stocks and flows. Among others, the dataset also includes: fields of study (and publications) labelled by their discipline(s); abstracts and linguistic features, i.e., standard language codes, tokens , and types entities’ general information, e.g., date of foundation and type of institutions; and academia related metrics, i.e., h-index. The resulting dataset maintains all the characteristics of the parent datasets and includes a set of additional subsets and data that can be used for new case studies relating to network analysis, knowledge exchange, linguistics, computational linguistics, and mobility and human migration, among others.增强型微软学术知识图谱（Enhanced Microsoft Academic Knowledge Graph，EMAKG）是一款涵盖学术出版物及相关实体的大型数据集，相关实体涵盖作者、科研机构、期刊、会议及研究领域。本数据集源自微软学术知识图谱（Microsoft Academic Knowledge Graph，MAKG）——当前规模最大的开源学术数据知识图谱之一。为构建该数据集，研究团队首先评估了现有MAKG的局限性，随后基于这些问题设计了多类数据增强方案，以拓展其应用场景，尤其在流动分析与网络分析领域应用价值显著。EMAKG主要具备两大核心优势：其一，可用性显著提升，方便非专业用户便捷获取与使用数据；其二，通过整合多源异构数据集与信息渠道，新增了更多类型的信息维度，进一步拓展了其应用范畴。例如，地理信息可支撑流动与移民相关研究。通过检索并合并最新版MAKG中已无法获取的出版物及其他实体信息，本知识图谱的完整性得到有效优化。此外，研究团队利用地理与合作网络的细节数据，提供了作者的年度所在地、职业国籍信息，以及全球范围内的年度存量与流动数据。除此之外，本数据集还涵盖以下内容：按学科标注的研究领域（及对应出版物）；摘要与语言特征，如标准语言代码、Token及实体类型标识；实体通用信息，例如科研机构的成立日期与机构类型；以及学术评价指标，如h指数（h-index）。最终生成的数据集完整保留了母数据集的全部特性，并新增了一系列子集与衍生数据，可用于网络分析、知识交流、语言学、计算语言学以及人口流动与移民研究等领域的全新案例研究。