Before revision:  We curate a dataset of scientific papers annotated according to our novel schema . Using an information extraction model trained on this new corpus, we construct a knowledge base (KB) of 2M mechanism relations, which we make publicly available. Our model is able to extract relations at an F1 at least twice that of baselines such as open IE or related scientific IE systems. We conduct experiments examining the ability of our system to retrieve relevant information on viral mechanisms of action, and on applications of AI to COVID-19 research.
After revision:  We curate 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 research.