Extracting a Knowledge Base of Mechanisms from COVID-19 Papers
This provides an automated tool for researchers and clinicians to better access interdisciplinary knowledge from COVID-19 papers, though it is incremental in applying existing extraction methods to a new domain.
The authors tackled the challenge of navigating the vast COVID-19 literature by constructing a knowledge base of mechanisms extracted from scientific papers, which outperformed PubMed search in a study with clinical experts.
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.