On the extraction of meaningful RNA interactions from Scientific Publications through LLMs and SPIRES

Knowledge graphs (KGs) are useful tools to uniformly represent and integrate heterogeneous information about a domain
of interest. However, they are inherently incomplete; therefore, new facts should be introduced by extracting them from
structured and unstructured data sources. Starting from RNA-KG, the first KG tailored for representing different kinds of
RNA molecules that we recently developed, in this paper we evaluate the use of SPIRES for extracting interactions among
bio-entities involving RNA molecules from scientific papers guided by the RNA-KG schema. SPIRES is a general-purpose
knowledge extraction system for mining information conforming to a specified schema. A customized prompt is generated
and submitted to a Large Language Model (LLM) along with a text to extract a set of RDF triples adhering to the schema
constraints. The experiments show a high accuracy in extracting interactions from the scientific literature.