Automatic Knowledge Base Construction from Scholarly Documents

The continuing growth of published scholarly content on the web ensures the availability of the most recent scientific findings to researchers. Scholarly documents, such as research articles, are easily accessed by using academic search engines that are built on large repositories of scholarly documents. Scientific information extraction from documents into a structured knowledge graph representation facilitates automated machine understanding of a document's content. Traditional information extraction approaches, that either require training samples or a preexisting knowledge base to assist in the extraction, can be challenging when applied to large repositories of digital documents. Labeled training examples for such large scale are difficult to obtain for such datasets. Also, most available knowledge bases are built from web data and do not have sufficient coverage to include concepts found in scientific articles. In this paper we aim to construct a knowledge graph from scholarly documents while addressing both these issues. We propose a fully automatic, unsupervised system for scientific information extraction that does not build on an existing knowledge base and avoids manually-tagged training data. We describe and evaluate a constructed taxonomy that contains over 15k entities resulting from applying our approach to 10k documents.