  Explaining neural model predictions to users requires creativity. Especially
in enterprise applications, where there are costs associated with users' time,
and their trust in the model predictions is critical for adoption. For link
prediction in master data management, we have built a number of explainability
solutions drawing from research in interpretability, fact verification, path
ranking, neuro-symbolic reasoning and self-explaining AI. In this demo, we
present explanations for link prediction in a creative way, to allow users to
choose explanations they are more comfortable with.
