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 otherwise be tedious. First, we briefly explain
our knowledge synthesis process to extract helpful information from
unstructured 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 demonstrate
the effectiveness and value-add.

本研究采用知识综合、段落检索、三元组检索和复杂问答等方法，构建一个可扩展的解决方案，以有效地从大规模研究文档中提取和探索复杂信息，以解决医疗保健行业面临的大量非结构化数据的问题，并在 COVID-19 数据集上进行了定性评估以证明其有效性。