Extracting a Knowledge Base of Mechanisms from COVID-19 Papers

Abstract

The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge. We pursue the construction of a knowledge base (KB) of mechanisms -- a fundamental concept across the sciences encompassing activities, functions and causal relations, ranging from cellular processes to economic impacts. We extract this information from the natural language of scientific papers by developing a broad, unified schema that strikes a balance between relevance and breadth. We annotate a dataset of mechanisms with our schema and train a model to extract mechanism relations from papers. Our experiments demonstrate the utility of our KB in supporting interdisciplinary scientific search over COVID-19 literature, outperforming the prominent PubMed search in a study with clinical experts.

Accepted to NAACL 2021 (long paper). Tom Hope and Aida Amini made an equal contribution. Data and code: https://git.io/JUhv7

References in corpus (5)

• CORD-19: The COVID-19 Open Research Dataset
• Simple BERT Models for Relation Extraction and Semantic Role Labeling
• COVID-19 SignSym: a fast adaptation of a general clinical NLP tool to identify and normalize COVID-19 signs and symptoms to OMOP common data model
• Information Mining for COVID-19 Research From a Large Volume of Scientific Literature
• COVID-SEE: Scientific Evidence Explorer for COVID-19 Related Research