We present a novel system that automatically extracts and generates informative and descriptive sentences from the biomedical corpus and facilitates the efficient search for relational knowledge. Unlike previous search engines or exploration systems that retrieve unconnected passages, our system organizes descriptive sentences as a relational graph, enabling researchers to explore closely related biomedical entities (e.g., diseases treated by a chemical) or indirectly connected entities (e.g., potential drugs for treating a disease). Our system also uses ChatGPT and a fine-tuned relation synthesis model to generate concise and reliable descriptive sentences from retrieved information, reducing the need for extensive human reading effort. With our system, researchers can easily obtain both high-level knowledge and detailed references and interactively steer to the information of interest. We spotlight the application of our system in COVID-19 research, illustrating its utility in areas such as drug repurposing and literature curation.

翻译：我们提出了一种新颖系统，能够自动从生物医学语料库中提取并生成信息丰富且具描述性的句子，从而促进关系知识的高效检索。与以往检索无关片段搜索引擎或探索系统不同，我们的系统将描述性句子组织为关系图谱，使研究者能够探索紧密关联的生物医学实体（如治疗某种疾病的化学物质）或间接关联实体（如治疗某种疾病的潜在药物）。该系统同时利用ChatGPT与微调后的关系合成模型，从检索信息中生成简洁可靠的描述性句子，显著减少人工阅读理解的工作量。借助本系统，研究者既能快速获取高层级知识，又能获得详细参考文献，并交互式地定位至感兴趣的信息。我们重点展示了该系统在COVID-19研究中的应用，证实其在药物重定位和文献整理等领域的实用价值。