In the expansion of biomedical dataset, the same category may be labeled with different terms, thus being tedious and onerous to curate these terms. Therefore, automatically mapping synonymous terms onto the ontologies is desirable, which we name as biomedical synonym prediction task. Unlike biomedical concept normalization (BCN), no clues from context can be used to enhance synonym prediction, making it essential to extract graph features from ontology. We introduce an expert-curated dataset OBO-syn encompassing 70 different types of concepts and 2 million curated concept-term pairs for evaluating synonym prediction methods. We find BCN methods perform weakly on this task for not making full use of graph information. Therefore, we propose GraphPrompt, a prompt-based learning approach that creates prompt templates according to the graphs. GraphPrompt obtained 37.2\% and 28.5\% improvement on zero-shot and few-shot settings respectively, indicating the effectiveness of these graph-based prompt templates. We envision that our method GraphPrompt and OBO-syn dataset can be broadly applied to graph-based NLP tasks, and serve as the basis for analyzing diverse and accumulating biomedical data. All the data and codes are avalible at: https://github.com/HanwenXuTHU/GraphPrompt

翻译：在生物医学数据集的扩展过程中，同一类别可能以不同术语标注，导致术语整理工作繁琐耗时。因此，将同义术语自动映射到本体体系成为迫切需求，我们将其命名为生物医学同义词预测任务。与生物医学概念标准化（BCN）不同，该任务无法利用上下文线索增强同义词预测，这使得从本体中提取图谱特征至关重要。我们引入由专家整理的OBO-syn数据集，涵盖70种不同类型的概念和200万组人工标注的概念-术语对，用于评估同义词预测方法。研究发现BCN方法因未能充分利用图谱信息而在该任务上表现欠佳。为此，我们提出GraphPrompt——一种基于提示学习的方法，根据图谱结构创建提示模板。GraphPrompt在零样本和少样本场景下分别取得37.2%和28.5%的性能提升，证明基于图谱的提示模板的有效性。我们展望GraphPrompt方法与OBO-syn数据集可广泛应用于基于图谱的自然语言处理任务，并为分析多样化且持续增长的生物医学数据奠定基础。所有数据与代码均可在 https://github.com/HanwenXuTHU/GraphPrompt 获取。