  In this paper, we present an automatic knowledge base construction system
from large scale enterprise documents with minimal efforts of human
intervention. In the design and deployment of such a knowledge mining system
for enterprise, we faced several challenges including data distributional
shift, performance evaluation, compliance requirements and other practical
issues. We leveraged state-of-the-art deep learning models to extract
information (named entities and definitions) at per document level, then
further applied classical machine learning techniques to process global
statistical information to improve the knowledge base. Experimental results are
reported on actual enterprise documents. This system is currently serving as
part of a Microsoft 365 service.
