微软学术知识图谱增强:作者姓名消歧、出版物分类和嵌入

IF 4.1 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE
Michael Färber, Lin Ao
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引用次数: 17

摘要

虽然在学术领域已经提出了一些大型知识图谱,但这些图谱在准确性和覆盖范围等几个数据质量维度上受到限制。在本文中,我们提出了增强微软学术知识图谱(MAKG)的方法,这是一个最近发布的大规模知识图谱,包含有关科学出版物和相关作者、场所和附属机构的元数据。在对MAKG进行定性分析的基础上,我们从三个方面进行了探讨。首先,我们采用并评估了大规模作者姓名消歧的无监督方法。其次,我们开发和评估按学科和关键词标记出版物的方法,促进出版物和相关实体的增强搜索和推荐。第三,基于几种最先进的嵌入技术,我们计算和评估了MAKG中所有2.39亿出版物、2.43亿作者、49,000种期刊和16,000个会议实体的嵌入。最后,我们为更新后的MAKG提供统计信息。我们最终的MAKG可在https://makg.org上公开获取,可用于搜索或推荐学术实体,以及增强的科学影响量化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Microsoft Academic Knowledge Graph enhanced: Author name disambiguation, publication classification, and embeddings
Abstract Although several large knowledge graphs have been proposed in the scholarly field, such graphs are limited with respect to several data quality dimensions such as accuracy and coverage. In this article, we present methods for enhancing the Microsoft Academic Knowledge Graph (MAKG), a recently published large-scale knowledge graph containing metadata about scientific publications and associated authors, venues, and affiliations. Based on a qualitative analysis of the MAKG, we address three aspects. First, we adopt and evaluate unsupervised approaches for large-scale author name disambiguation. Second, we develop and evaluate methods for tagging publications by their discipline and by keywords, facilitating enhanced search and recommendation of publications and associated entities. Third, we compute and evaluate embeddings for all 239 million publications, 243 million authors, 49,000 journals, and 16,000 conference entities in the MAKG based on several state-of-the-art embedding techniques. Finally, we provide statistics for the updated MAKG. Our final MAKG is publicly available at https://makg.org and can be used for the search or recommendation of scholarly entities, as well as enhanced scientific impact quantification.
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来源期刊
Quantitative Science Studies
Quantitative Science Studies INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
12.10
自引率
12.50%
发文量
46
审稿时长
22 weeks
期刊介绍:
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