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We present a novel system that automatically extracts and generatesinformative and descriptive sentences from the biomedical corpus andfacilitates the efficient search for relational knowledge. Unlike previoussearch engines or exploration systems that retrieve unconnected passages, oursystem organizes descriptive sentences as a relational graph, enablingresearchers to explore closely related biomedical entities (e.g., diseasestreated by a chemical) or indirectly connected entities (e.g., potential drugsfor treating a disease). Our system also uses ChatGPT and a fine-tuned relationsynthesis model to generate concise and reliable descriptive sentences fromretrieved information, reducing the need for extensive human reading effort.With our system, researchers can easily obtain both high-level knowledge anddetailed references and interactively steer to the information of interest. Wespotlight the application of our system in COVID-19 research, illustrating itsutility in areas such as drug repurposing and literature curation.

Descriptive Knowledge Graph in Biomedical Domain