论文标题
知识图验证
Knowledge Graph Validation
论文作者
论文摘要
知识图（KGS）已证明是Google和Microsoft等大型公司的重要资产。 KGS在提供结构化和语义上丰富的信息，使它们提供给人和机器，并提供准确，正确和可靠的知识方面发挥着重要作用。这样做的一项关键任务是知识验证，它衡量了KGS的语句在语义上是否正确，并且与所谓的“真实”世界相对应。在本文中，我们对KGS知识验证的最新方法，方法和工具以及对它们的评估进行了概述和审查。结果，我们证明了工具结果缺乏可重复性，提供见解并陈述我们未来的研究方向。
Knowledge graphs (KGs) have shown to be an important asset of large companies like Google and Microsoft. KGs play an important role in providing structured and semantically rich information, making them available to people and machines, and supplying accurate, correct and reliable knowledge. To do so a critical task is knowledge validation, which measures whether statements from KGs are semantically correct and correspond to the so-called "real" world. In this paper, we provide an overview and review of the state-of-the-art approaches, methods and tools on knowledge validation for KGs, as well as an evaluation of them. As a result, we demonstrate a lack of reproducibility of tools results, give insights, and state our future research direction.
