Automatic Construction of Enterprise Knowledge Base
This work addresses the challenge of building knowledge bases for enterprises, though it appears incremental by combining existing methods.
The paper tackles the problem of automatically constructing an enterprise knowledge base from large-scale documents with minimal human intervention, reporting experimental results on actual enterprise documents and deployment as part of a Microsoft 365 service.
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.