Before revision:  In this work, we present a system that uses rule based annotators to augment training data for neural models, and for slot filling to increase the diversity of the populated knowledge graph. We also propose a representative set sampling method to use the populated knowledge graph data for downstream applications. We introduce new resources and discuss our results .
After revision:  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 .