Automatic Construction of Enterprise Knowledge Base

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,...

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Bibliographic Details
Published inarXiv.org
Main Authors Chai, Junyi, He, Yujie, Hashemi, Homa, Li, Bing, Daraksha Parveen, Ranganath Kondapally, Xu, Wenjin
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 29.06.2021
Subjects
Deep learning
Knowledge bases (artificial intelligence)
Knowledge management
Machine learning
Performance evaluation
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Summary: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.
ISSN:2331-8422