Objective. Graphical abstracts are small graphs of concepts that visually
summarize the main findings of scientific articles. While graphical abstracts
are customarily used in scientific publications to anticipate and summarize
their main results, we propose them as a means for expressing graph searches
over existing literature. Materials and methods. We consider the COVID-19 Open
Research Dataset (CORD-19), a corpus of more than one million abstracts; each
of them is described as a graph of co-occurring ontological terms, selected
from the Unified Medical Language System (UMLS) and the Ontology of Coronavirus
Infectious Disease (CIDO). Graphical abstracts are also expressed as graphs of
ontological terms, possibly augmented by utility terms describing their
interactions (e.g., "associated with", "increases", "induces"). We build a
co-occurrence network of concepts mentioned in the corpus; we then identify the
best matches of graphical abstracts on the network. We exploit graph database
technology and shortest-path queries. Results. We build a large co-occurrence
network, consisting of 128,249 entities and 47,198,965 relationships. A
well-designed interface allows users to explore the network by formulating or
adapting queries in the form of an abstract; it produces a bibliography of
publications, globally ranked; each publication is further associated with the
specific parts of the abstract that it explains, thereby allowing the user to
understand each aspect of the matching. Discussion and Conclusion. Our approach
supports the process of scientific hypothesis formulation and evidence search;
it can be reapplied to any scientific domain, although our mastering of UMLS
makes it most suited to clinical domains.Comment: 12 pages, 6 figure