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

Extracting a Knowledge Base of Mechanisms from COVID-19 Papers