Descriptive Knowledge Graph in Biomedical Domain
Kerui Zhu Jie Huang Kevin Chen-Chuan Chang
We present a novel system that automatically extracts and generates
informative and descriptive sentences from the biomedical corpus and
facilitates the efficient search for relational knowledge. Unlike previous
search engines or exploration systems that retrieve unconnected passages, our
system organizes descriptive sentences as a relational graph, enabling
researchers to explore closely related biomedical entities (e.g., diseases
treated by a chemical) or indirectly connected entities (e.g., potential drugs
for treating a disease). Our system also uses ChatGPT and a fine-tuned relation
synthesis model to generate concise and reliable descriptive sentences from
retrieved information, reducing the need for extensive human reading effort.
With our system, researchers can easily obtain both high-level knowledge and
detailed references and interactively steer to the information of interest. We
spotlight the application of our system in COVID-19 research, illustrating its
utility in areas such as drug repurposing and literature curation.