  In this work we propose a novel end-to-end multi-stage Knowledge Graph (KG)
generation system from textual inputs, separating the overall process into two
stages. The graph nodes are generated first using pretrained language model,
followed by a simple edge construction head, enabling efficient KG extraction
from the text. For each stage we consider several architectural choices that
can be used depending on the available training resources. We evaluated the
model on a recent WebNLG 2020 Challenge dataset, matching the state-of-the-art
performance on text-to-RDF generation task, as well as on New York Times (NYT)
and a large-scale TekGen datasets, showing strong overall performance,
outperforming the existing baselines. We believe that the proposed system can
serve as a viable KG construction alternative to the existing linearization or
sampling-based graph generation approaches. Our code can be found at
https://github.com/IBM/Grapher
