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