论文标题
具有NLP辅助注释的知识管理系统：简短的调查和前景
Knowledge Management System with NLP-Assisted Annotations: A Brief Survey and Outlook
论文作者
论文摘要
知识管理系统（KMS）对工业研究人员，化学或研究企业或基于证据的决策的需求很高。但是，现有系统在分类和组织纸张见解或关系方面存在局限性。传统数据库通常与记录系统不相交，这限制了其在简明的，整理概述中的实用性。在这项工作中，我们简要调查了该问题空间的现有方法，并提出了一个统一的框架，该框架利用关系数据库记录层次结构信息以促进研究和写作过程，或从连接概念中从参考文献或见解中产生有用的知识。我们的双向知识管理系统（BKMS）的框架可实现包括改进的层次结构笔记，AI协助的头脑风暴和多向关系的新功能。潜在的应用程序包括管理库存和制造或研究企业的变更，或通过基于证据的决策生成分析报告。
Knowledge management systems (KMS) are in high demand for industrial researchers, chemical or research enterprises, or evidence-based decision making. However, existing systems have limitations in categorizing and organizing paper insights or relationships. Traditional databases are usually disjoint with logging systems, which limit its utility in generating concise, collated overviews. In this work, we briefly survey existing approaches of this problem space and propose a unified framework that utilizes relational databases to log hierarchical information to facilitate the research and writing process, or generate useful knowledge from references or insights from connected concepts. Our framework of bidirectional knowledge management system (BKMS) enables novel functionalities encompassing improved hierarchical note-taking, AI-assisted brainstorming, and multi-directional relationships. Potential applications include managing inventories and changes for manufacture or research enterprises, or generating analytic reports with evidence-based decision making.
