The quality of a Knowledge Graph (also known as Linked Data) is an important aspect to indicate its fitness for use in an application. Several quality dimensions are identified, such as accuracy, completeness, timeliness, provenance, and accessibility, which are used to assess the quality. While many prior studies offer a landscape view of data quality dimensions, here we focus on presenting a systematic literature review for assessing the completeness of Knowledge Graph. We gather existing approaches from the literature and analyze them qualitatively and quantitatively. In particular, we unify and formalize commonly used terminologies across 56 articles related to the completeness dimension of data quality and provide a comprehensive list of methodologies and metrics used to evaluate the different types of completeness. We identify seven types of completeness, including three types that were not previously identified in previous surveys. We also analyze nine different tools capable of assessing Knowledge Graph completeness. The aim of this Systematic Literature Review is to provide researchers and data curators a comprehensive and deeper understanding of existing works on completeness and its properties, thereby encouraging further experimentation and development of new approaches focused on completeness as a data quality dimension of Knowledge Graph.

中文翻译：

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知识图完整性：系统文献综述

知识图（也称为链接数据）的质量是表明其适用于应用程序的重要方面。确定了几个质量维度，例如准确性，完整性，及时性，出处和可访问性，用于评估质量。尽管许多先前的研究提供了数据质量维度的概况，但在这里我们着重介绍系统的文献综述，以评估知识图的完整性。我们从文献中收集现有方法，并进行定性和定量分析。特别是，我们在56条与数据质量完整性维度有关的文章中统一并规范了常用术语，并提供了用于评估不同类型完整性的方法和指标的全面列表。我们确定了七种完整性，其中包括先前调查中未发现的三种类型。我们还分析了九种能够评估知识图完整性的工具。本系统文献综述的目的是为研究人员和数据管理员提供关于完整性及其属性的现有作品的全面，更深刻的理解，从而鼓励进一步试验和开发以完整性为重点的新方法，并将其作为知识图的数据质量维度。