Objective: To develop a high-throughput biomedical relation extraction system that takes advantage of the large language models'(LLMs) reading comprehension ability and biomedical world knowledge in a scalable and evidential manner. Methods: We formulate the relation extraction task as binary classifications for large language models. Specifically, LLMs make the decision based on the external corpus and its world knowledge, giving the reason for the judgment for factual verification. This method is tailored for semi-structured web articles, wherein we designate the main title as the tail entity and explicitly incorporate it into the context, and the potential head entities are matched based on a biomedical thesaurus. Moreover, lengthy contents are sliced into text chunks, embedded, and retrieved with additional embedding models. Results: Using an open-source LLM, we extracted 248659 relation triplets of three distinct relation types from three reputable biomedical websites. To assess the efficacy of the basic pipeline employed for biomedical relation extraction, we curated a benchmark dataset annotated by a medical expert. Evaluation results indicate that the pipeline exhibits performance comparable to that of GPT-4. Case studies further illuminate challenges faced by contemporary LLMs in the context of biomedical relation extraction for semi-structured web articles. Conclusion: The proposed method has demonstrated its effectiveness in leveraging the strengths of LLMs for high-throughput biomedical relation extraction. Its adaptability is evident, as it can be seamlessly extended to diverse semi-structured biomedical websites, facilitating the extraction of various types of biomedical relations with ease.

翻译：目的：开发一种高通量生物医学关系抽取系统，利用大语言模型的阅读理解能力和生物医学世界知识实现可扩展且可验证的关系抽取。方法：将关系抽取任务形式化为大语言模型的二元分类问题。具体而言，大语言模型基于外部语料库及其世界知识进行决策，并给出判断理由以支持事实验证。该方法针对半结构化网络文章进行优化，将主标题指定为尾实体并显式融入上下文，同时基于生物医学词表匹配潜在的头实体。此外，长篇幅内容被切分为文本片段，通过嵌入模型进行向量化、检索及增强处理。结果：使用开源大语言模型，我们从三个权威生物医学网站中提取了248,659个关系三元组，涵盖三种不同的关系类型。为评估基础流程在生物医学关系抽取中的有效性，我们构建了由医学专家标注的基准数据集。评估结果表明，该流程的性能与GPT-4相当。案例研究进一步揭示了当代大语言模型在面向半结构化网络文章的生物医学关系抽取中面临的挑战。结论：所提方法有效发挥了大语言模型在高通量生物医学关系抽取中的优势，其适应性显著，可无缝扩展至各类半结构化生物医学网站，便捷地提取多种类型的生物医学关系。