  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.
