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.

翻译：近期医疗行业的进步导致非结构化数据大量涌现，使得大规模高效准确的信息检索等任务面临挑战。本研究提出一种一体化可扩展解决方案，用于从大规模研究文档中提取和探索复杂信息，有效规避传统方法的繁琐性。首先，我们简要阐述从研究文档非结构化文本数据中提取有用信息的知识综合流程。随后，基于已提取的知识，通过段落检索、知识图谱三元组检索和复杂问答三大核心组件实现复杂信息检索。这些组件融合词汇与语义方法，实现段落与三元组检索，并通过分面精炼技术对检索结果进行过滤。生物医学查询与文档的复杂性要求问答系统具备处理超越事实型查询的能力，我们通过COVID-19开放研究数据集（CORD-19）进行定性评估，验证了本方法的有效性与增值价值。