  The COVID-19 pandemic has driven ever-greater demand for tools which enable
efficient exploration of biomedical literature. Although semi-structured
information resulting from concept recognition and detection of the defining
elements of clinical trials (e.g. PICO criteria) has been commonly used to
support literature search, the contributions of this abstraction remain poorly
understood, especially in relation to text-based retrieval. In this study, we
compare the results retrieved by a standard search engine with those filtered
using clinically-relevant concepts and their relations. With analysis based on
the annotations from the TREC-COVID shared task, we obtain quantitative as well
as qualitative insights into characteristics of relational and concept-based
literature exploration. Most importantly, we find that the relational concept
selection filters the original retrieved collection in a way that decreases the
proportion of unjudged documents and increases the precision, which means that
the user is likely to be exposed to a larger number of relevant documents.

Computation and Language;Information Retrieval