  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.
