Automatic Construction of Enterprise Knowledge Base
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Enabling LLM Knowledge Analysis via Extensive Materialization
Project SHADOW: Symbolic Higher-order Associative Deductive reasoning On Wikidata using LM probing
CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models
Expanding the Vocabulary of BERT for Knowledge Base Construction
MAPS-KB: A Million-scale Probabilistic Simile Knowledge Base
Russian Web Tables: A Public Corpus of Web Tables for Russian Language Based on Wikipedia
토론
이 논문과 연결된 포럼 글이 아직 없습니다.