  Following the recent successful examples of large technology companies, many
modern enterprises seek to build knowledge graphs to provide a unified view of
corporate knowledge and to draw deep insights using machine learning and
logical reasoning. There is currently a perceived disconnect between the
traditional approaches for data science, typically based on machine learning
and statistical modelling, and systems for reasoning with domain knowledge. In
this paper we present a state-of-the-art Knowledge Graph Management System,
Vadalog, which delivers highly expressive and efficient logical reasoning and
provides seamless integration with modern data science toolkits, such as the
Jupyter platform. We demonstrate how to use Vadalog to perform traditional data
wrangling tasks, as well as complex logical and probabilistic reasoning. We
argue that this is a significant step forward towards combining machine
learning and reasoning in data science.
