论文标题
保存知识图的调查和开放问题：合并，查询，表示，完成和应用程序
Survey and Open Problems in Privacy Preserving Knowledge Graph: Merging, Query, Representation, Completion and Applications
论文作者
论文摘要
知识图（KG）吸引了越来越多的公司的注意力，因为其能够以有意义的方式连接不同类型的数据并支持丰富的数据服务。但是，数据隔离问题限制了KG的性能，并防止其进一步发展。也就是说，多个政党有自己的KG，但由于监管或竞争原因，他们无法彼此分享。因此，如何执行保留KG的隐私成为要回答的重要研究问题。也就是说，多个政党根据保护多个公斤的隐私进行协作进行公共相关任务。迄今为止，解决上述kg隔离问题的工作很少。在本文中，为了填补这一空白，我们总结了在数据隔离设置中保留kg的开放问题，并为其提出了可能的解决方案。具体而言，我们总结了从四个方面（即合并，查询，表示和完成）中保存kg的开放问题。我们详细介绍了这些问题，并为它们提出了可能的技术解决方案。此外，我们提出了三个保留KG了解应用程序的隐私，并简单地描述了如何将我们提出的技术应用于这些应用程序。
Knowledge Graph (KG) has attracted more and more companies' attention for its ability to connect different types of data in meaningful ways and support rich data services. However, the data isolation problem limits the performance of KG and prevents its further development. That is, multiple parties have their own KGs but they cannot share with each other due to regulation or competition reasons. Therefore, how to conduct privacy preserving KG becomes an important research question to answer. That is, multiple parties conduct KG related tasks collaboratively on the basis of protecting the privacy of multiple KGs. To date, there is few work on solving the above KG isolation problem. In this paper, to fill this gap, we summarize the open problems for privacy preserving KG in data isolation setting and propose possible solutions for them. Specifically, we summarize the open problems in privacy preserving KG from four aspects, i.e., merging, query, representation, and completion. We present these problems in details and propose possible technical solutions for them. Moreover, we present three privacy preserving KG-aware applications and simply describe how can our proposed techniques be applied into these applications.
