  Ontologies comprising of concepts, their attributes, and relationships, form
the quintessential backbone of many knowledge based AI systems. These systems
manifest in the form of question-answering or dialogue in number of business
analytics and master data management applications. 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.
