  Understanding large, structured documents like scholarly articles, requests
for proposals or business reports is a complex and difficult task. It involves
discovering a document's overall purpose and subject(s), understanding the
function and meaning of its sections and subsections, and extracting low level
entities and facts about them. In this research, we present a deep learning
based document ontology to capture the general purpose semantic structure and
domain specific semantic concepts from a large number of academic articles and
business documents. The ontology is able to describe different functional parts
of a document, which can be used to enhance semantic indexing for a better
understanding by human beings and machines. We evaluate our models through
extensive experiments on datasets of scholarly articles from arXiv and Request
for Proposal documents.
