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 a simple binary classification problem for large language models such as ChatGPT. Specifically, LLMs make the decision based on the external corpus and its world knowledge, giving the reason for the judgment to 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, ensuring compatibility with the context window size constraints of available open-source LLMs. Results: Using an open-source LLM, we extracted 304315 relation triplets of three distinct relation types from four 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（LLMs）的阅读理解能力和生物医学领域知识，实现可扩展且可验证的关系提取。方法：将关系抽取任务转化为大语言模型（如ChatGPT）的简单二分类问题。具体而言，LLMs基于外部语料库和其世界知识进行决策，并为判断结果提供事实核查依据。该方法专为半结构化网络文章设计，我们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，将长文本切分为文本块，通过嵌入模型进行向量化存储与检索，以确保与现有开源LLMs的上下文窗口大小约束兼容。结果：利用开源LLM，从四个权威生物医学网站中提取了304315个包含三种关系类型的关系三元组。为评估生物医学关系抽取基础流程的有效性，我们构建了由医学专家标注的基准数据集。评估结果表明，该流程性能与GPT-4相当。案例分析进一步揭示了当前LLMs在半结构化网络文章生物医学关系抽取场景中所面临的挑战。结论：所提方法有效验证了LLMs在高通量生物医学关系抽取中的优势能力，其可扩展性显著，能无缝应用于多样化半结构化生物医学网站，支持多种类型生物医学关系的便捷提取。