SBIR 136 - OMNISYNC: A Real-Time Comprehensive Knowledge Graph for the Biomedical Science Community

The modern biomedical landscape is punctuated by a surge of innovative findings, a substantial chunk of which resides in academic papers and esteemed journal publications. While these documents harbor groundbreaking results, their intricate and textual nature often leaves the knowledge latent and underutilized. Our proposed methodology aims to bridge this gap by representing these critical insights as structured Knowledge Graphs (KGs). The end-product proposed—a dynamically enriched biomedical Knowledge Graph (KG) integrated with the latest research insights and powered by advanced machine learning models—offers several significant advantages over current methodologies and technologies: Successful completion of this technical objective would prove that training data can be vastly expanded based on a huge corpus of academic works that we have already indexed in a vector database.