  Ontologies comprising of concepts, their attributes, and relationships are
used in many knowledge based AI systems. While there have been efforts towards
populating domain specific ontologies, we examine the role of document
structure in learning ontological relationships between concepts in any
document corpus. Inspired by ideas from hypernym discovery and explainability,
our method performs about 15 points more accurate than a stand-alone R-GCN
model for this task.
