  Cold start knowledge base population (KBP) is the problem of populating a
knowledge base from unstructured documents. While artificial neural networks
have led to significant improvements in the different tasks that are part of
KBP, the overall F1 of the end-to-end system remains quite low. This problem is
more acute in personal knowledge bases, which present additional challenges
with regard to data protection, fairness and privacy. In this work, we present
a system that uses rule based annotators and a graph neural network for missing
link prediction, to populate a more complete, fair and diverse knowledge base
from the TACRED dataset.
