  The performance of applications, such as personal assistants and search
engines, relies 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 KGs are semantically
correct. KGs inevitably contain incorrect and incomplete statements, which may
hinder their adoption in business applications as they are not trustworthy. In
this paper, we propose and implement a Validator that computes a confidence
score for every triple and instance in KGs. The computed score is based on
finding the same instances across different weighted knowledge sources and
comparing their features. We evaluate our approach by comparing its results
against a baseline validation. Our results suggest that we can validate KGs
with an f-measure of at least 75%. Time-wise, the Validator, performed a
validation of 2530 instances in 15 minutes approximately. Furthermore, we give
insights and directions toward a better architecture to tackle KG validation.
