The performance of applications, such as personal assistants, search engines, and question-answering systems, rely on high-quality knowledge bases, a.k.a. Knowledge Graphs (KGs). To ensure their quality one important task is Knowledge Validation, which measures the degree to which statements or triples of a Knowledge Graph (KG) are correct. KGs inevitably contains incorrect and incomplete statements, which may hinder the adoption of such KGs in business applications as they are not trustworthy. In this paper, we propose and implement a validation approach that computes a confidence score for every triple and instance in a KG. The computed score is based on finding the same instances across different weighted knowledge sources and comparing their features. We evaluated the performance of our Validator by comparing a manually validated result against the output of the Validator. The experimental results showed that compared with the manual validation, our Validator achieved as good precision as the manual validation, although with certain limitations. Furthermore, we give insights and directions toward a better architecture to tackle KG validation.

中文翻译：

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通过加权知识源进行知识图验证

诸如个人助理，搜索引擎和问题解答系统之类的应用程序的性能取决于高质量的知识库，即知识图（KG）。为了确保其质量，一项重要的任务是知识验证，它衡量知识图（KG）的陈述或三元组正确的程度。KG不可避免地包含不正确和不完整的声明，这可能会阻碍此类KG在业务应用程序中的采用，因为它们是不可信的。在本文中，我们提出并实现了一种验证方法，该方法可以计算KG中每个三元组和实例的置信度得分。计算出的分数基于在不同加权知识源中找到相同实例并比较其特征。我们通过将手动验证的结果与验证器的输出进行比较，评估了验证器的性能。实验结果表明，与人工验证相比，我们的Validator达到了与人工验证一样好的精度，尽管有一定的局限性。此外，我们为解决KG验证的更好架构提供了见解和方向。