论文标题
知识分线：科学知识基础构建的AI-IN-IN-IN-in-in-in-in-in-in-in-in-in-inop文档注释系统
KnowledgeShovel: An AI-in-the-Loop Document Annotation System for Scientific Knowledge Base Construction
论文作者
论文摘要
构建一个全面，准确且有用的科学知识基础对于人类研究人员综合科学知识和实现AL驱动的科学发现至关重要。但是，由于（1）可用的大量科学文献，当前的过程很困难，容易出错并且费力。 （2）高度专业的科学领域； （3）信息的多种方式（文本，图，表）； （4）不同出版物中的科学知识的孤岛，格式和结构不一致。在一项形成性研究的情况下，我们设计了参与式设计研讨会，我们设计并开发了知识销售，这是一个在环境文档注释系统中，供研究人员构建科学知识基础。 KnowledGeshovel的设计引入了多个模式的人类AI协作管道，该管道与用户现有的工作流程保持一致，以提高数据准确性，同时减轻人类负担。对7个地球科学研究人员进行的后续用户评估表明，知识群可以以令人满意的精确性来有效地构建科学知识库。
Constructing a comprehensive, accurate, and useful scientific knowledge base is crucial for human researchers synthesizing scientific knowledge and for enabling Al-driven scientific discovery. However, the current process is difficult, error-prone, and laborious due to (1) the enormous amount of scientific literature available; (2) the highly-specialized scientific domains; (3) the diverse modalities of information (text, figure, table); and, (4) the silos of scientific knowledge in different publications with inconsistent formats and structures. Informed by a formative study and iterated with participatory design workshops, we designed and developed KnowledgeShovel, an Al-in-the-Loop document annotation system for researchers to construct scientific knowledge bases. The design of KnowledgeShovel introduces a multi-step multi-modal human-AI collaboration pipeline that aligns with users' existing workflows to improve data accuracy while reducing the human burden. A follow-up user evaluation with 7 geoscience researchers shows that KnowledgeShovel can enable efficient construction of scientific knowledge bases with satisfactory accuracy.
