数据集知识图:为数据集创建链接的开放数据源

IF 4.1 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE
Michael Färber, David Lamprecht
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引用次数: 14

摘要

一些学术知识图已经被提出来建模和分析学术景观。然而,尽管近年来数据集的数量显著增加,但这些知识图谱并不主要关注数据集,而是关注相关实体,如出版物。此外,公开可用的数据集知识图没有系统地包含到提到数据集的出版物的链接。在本文中,我们提出了一种构建满足上述标准的RDF知识图的方法。我们的数据集知识图,DSKG,在http://dskg.org上公开提供,包含所有科学学科的数据集元数据。为了确保DSKG的高数据质量,我们首先确定适合创建DSKG的原始数据集集合。然后,我们在提到这些数据集的Microsoft学术知识图中建模的数据集和出版物之间建立链接。针对数据集作者姓名可能存在歧义的情况,我们开发并评估了一种作者姓名消歧义的方法,并通过ORCID链接丰富了知识图谱。总的来说,我们的知识图谱包含了2000多个具有相关属性的数据集,以及814000个指向635000个科学出版物的链接。它可以用于各种场景,促进高级数据集搜索系统和测量和授予数据集提供的新方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The data set knowledge graph: Creating a linked open data source for data sets
Abstract Several scholarly knowledge graphs have been proposed to model and analyze the academic landscape. However, although the number of data sets has increased remarkably in recent years, these knowledge graphs do not primarily focus on data sets but rather on associated entities such as publications. Moreover, publicly available data set knowledge graphs do not systematically contain links to the publications in which the data sets are mentioned. In this paper, we present an approach for constructing an RDF knowledge graph that fulfills these mentioned criteria. Our data set knowledge graph, DSKG, is publicly available at http://dskg.org and contains metadata of data sets for all scientific disciplines. To ensure high data quality of the DSKG, we first identify suitable raw data set collections for creating the DSKG. We then establish links between the data sets and publications modeled in the Microsoft Academic Knowledge Graph that mention these data sets. As the author names of data sets can be ambiguous, we develop and evaluate a method for author name disambiguation and enrich the knowledge graph with links to ORCID. Overall, our knowledge graph contains more than 2,000 data sets with associated properties, as well as 814,000 links to 635,000 scientific publications. It can be used for a variety of scenarios, facilitating advanced data set search systems and new ways of measuring and awarding the provisioning of data sets.
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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
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