KnowledgeShovel: An AI-in-the-Loop Document Annotation System for Scientific Knowledge Base Construction
This addresses the laborious and error-prone process of building scientific knowledge bases for researchers, but it is incremental as it builds on existing AI-in-the-loop and annotation system concepts.
The paper tackled the problem of constructing scientific knowledge bases from literature by developing KnowledgeShovel, an AI-in-the-loop annotation system, which a user evaluation with 7 geoscience researchers showed enables efficient construction with satisfactory accuracy.
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.