The urgency of mitigating COVID-19  has spawned a large and diverse body of scientific literature that is challenging for researchers to navigate. This explosion of information has stimulated interest in automated tools to help identify useful knowledge. We have pursued the use of methods for extracting diverse forms of mechanism relations from the natural language of scientific papers. We seek to identify concepts in COVID-19 and related literature which represent activities, functions , associations and causal relations, ranging from cellular processes to economic impacts. We formulate a broad, coarse-grained schema targeting mechanism relations between open, free-form entities. Our approach strikes a balance between expressivity and breadththat supports generalization across diverse concepts. 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. In both cases, our system identifies relevant information from our automatically-constructed knowledge base with high precision .
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 .
D
urgency of mitigating
A
pandemic
D
large and
R
for researchers to navigate. This explosion of information has stimulated
to navigate, stimulating
R
identify
find
R
have pursued the use of methods for extracting diverse forms of mechanism relations from the natural language of scientific papers. We seek to identify concepts in COVID-19 and related literature which represent
pursue the construction of a knowledge base (KB) of mechanisms -- a fundamental concept across the sciences encompassing
D
, associations
R
formulate
extract this information from the natural language of scientific papers by developing
R
coarse-grained schema targeting mechanism relations between open, free-form entities. Our approach
unified schema that
R
expressivity and breadththat supports generalization across diverse concepts. We curate
relevance and breadth. We annotate
R
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
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
R
research. In both cases, our system identifies relevant information from our automatically-constructed knowledge base with high precision
literature, outperforming the prominent PubMed search in a study with clinical experts