A Case Study in Bootstrapping Ontology Graphs from Textbooks
doi:10.24432/C58C7T
TL;DR
Baseline performance of BERT on entity and extraction from textbooks, and a novel labeling task for improving its performance Ontology graphs are graphs in which the nodes are generic classes and edges have labels that specify the relationships between the classes. In this paper, we address the question:to what extent can automated extraction and crowdsourcing techniques be combined to boostrap the creation of comprehensive and accurate ontology knowledge graphs? By adapting the state-of-the-art language model BERT to the task, and leveraging a novel relationship selection task, we show that even though it is difficult to achieve a high precision and recall, automated term extraction and crowd sourcing provide a way to bootstrap the ontology graph creation for further refinement and improvement through human effort.
Citation
@inproceedings{
chaudhri2021a,
title={A Case Study in Bootstrapping Ontology Graphs from Textbooks},
author={Vinay K. Chaudhri and Matthew Boggess and Han Lin Aung and Debshila Basu Mallick and Andrew C Waters and Richard Baraniuk},
booktitle={3rd Conference on Automated Knowledge Base Construction},
year={2021},
url={https://openreview.net/forum?id=nDe2D8DDXKR},
doi={10.24432/C58C7T}
}