# KnowledgeShovel: An AI-in-the-Loop Document Annotation System for Scientific Knowledge Base Construction

arXiv: https://arxiv.org/abs/2210.02830 · HTML: https://mlsanity.com/paper/2210.02830/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 63 references that are in the map.
- First posted on arXiv 6 Oct 2022.

## Builds on (most cited first, 7 of 7)

- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [Construction of the Literature Graph in Semantic Scholar](https://mlsanity.com/paper/1805.02262/) (2018)
- [Whose AI Dream? In search of the aspiration in data annotation](https://mlsanity.com/paper/2203.10748/) (2022)
- [Extracting a Knowledge Base of Mechanisms from COVID-19 Papers](https://mlsanity.com/paper/2010.03824/) (2020)
- [ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts](https://mlsanity.com/paper/2205.06982/) (2022)
- [Information Extraction from Scientific Literature for Method Recommendation](https://mlsanity.com/paper/1901.00401/) (2018)
- [TableLab: An Interactive Table Extraction System with Adaptive Deep Learning](https://mlsanity.com/paper/2102.08445/) (2021)

## Built on by (most cited first, 2 of 2)

- [Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis](https://mlsanity.com/paper/2309.12368/) (2023)
- [AceMap: Knowledge Discovery through Academic Graph](https://mlsanity.com/paper/2403.02576/) (2024)

## Abstract (arXiv)

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.