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.
翻译:个人助理、搜索引擎和问答系统等应用程序的性能取决于高质量的知识基础,a.k.a.a.知识图(KGs)。为了确保质量,一项重要任务就是“知识验证”,衡量说明或知识图(KG)的三倍正确程度。KGs不可避免地包含不正确和不完整的报表,这可能妨碍在商业应用中采用这种KGs,因为它们不可信。在本文件中,我们提议并执行一项验证办法,计算出KG中每三重案例的信任分数。计算得分的依据是在不同加权知识来源中找到相同实例,并比较其特征。我们通过将人工验证结果与校验结果进行比较,对校验结果进行了评估。实验结果表明,与手动验证相比,我们的校验结果与手动验证相当精确,但有某些限制。此外,我们为改进KG的验证结构提供了深刻的见解和方向。