At present, although knowledge graphs have been widely used in various fields such as recommendation systems, question and answer systems, and intelligent search, there are always quality problems such as knowledge omissions and errors. Quality assessment and control, as an important means to ensure the quality of knowledge, can make the applications based on knowledge graphs more complete and more accurate by reasonably assessing the knowledge graphs and fixing and improving the quality problems at the same time. Therefore, as an indispensable part of the knowledge graph construction process, the results of quality assessment and control determine the usefulness of the knowledge graph. Among them, the assessment and enhancement of completeness, as an important part of the assessment and control phase, determine whether the knowledge graph can fully reflect objective phenomena and reveal potential connections among entities. In this paper, we review specific techniques of completeness assessment and classify completeness assessment techniques in terms of closed world assumptions, open world assumptions, and partial completeness assumptions. The purpose of this paper is to further promote the development of knowledge graph quality control and to lay the foundation for subsequent research on the completeness assessment of knowledge graphs by reviewing and classifying completeness assessment techniques.

中文翻译：

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知识图谱完整性度量技术分类

目前，虽然知识图谱已广泛应用于推荐系统、问答系统、智能搜索等各个领域，但始终存在知识遗漏、错误等质量问题。质量评估与控制作为保证知识质量的重要手段，通过对知识图谱的合理评估，同时修复和改进质量问题，可以使基于知识图谱的应用更加完整、更加准确。因此，作为知识图谱构建过程中不可或缺的一部分，质量评估和控制的结果决定了知识图谱的有用性。其中，完整性的评估和增强作为评估和控制阶段的重要组成部分，决定了知识图谱是否能够充分反映客观现象并揭示实体之间的潜在联系。在本文中，我们回顾了完整性评估的具体技术，并将完整性评估技术分为封闭世界假设、开放世界假设和部分完整性假设。本文的目的是通过对完整性评估技术的回顾和分类，进一步推动知识图谱质量控制的发展，并为后续知识图谱完整性评估的研究奠定基础。