  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.
