Large-Scale Knowledge Synthesis and Complex Information Retrieval from Biomedical Documents

Recent advances in the healthcare industry have led to an abundance of unstructured data, making it challenging perform tasks such as efficient and accurate information retrieval at scale. Our work offers all-in-one scalable solution for extracting exploring complex from large-scale research documents, which would otherwise be tedious. First, we briefly explain our knowledge synthesis process extract helpful text data documents. Then, on top extracted using three major components- Paragraph Retrieval, Triplet Retrieval Knowledge Graphs, Complex Question Answering (QA). These components combine lexical semantic-based methods retrieve paragraphs triplets faceted refinement filtering these search results. The complexity biomedical queries documents necessitates a QA system capable handling more than factoid queries, evaluate qualitatively COVID-19 Open Research Dataset (CORD-19) demonstrate effectiveness valueadd.