Aligning terminological resources, including ontologies, controlled vocabularies, taxonomies, and value sets is a critical part of data integration in many domains such as healthcare, chemistry, and biomedical research. Entity mapping is the process of determining correspondences between entities across these resources, such as gene identifiers, disease concepts, or chemical entity identifiers. Many tools have been developed to compute such mappings based on common structural features and lexical information such as labels and synonyms. Lexical approaches in particular often provide very high recall, but low precision, due to lexical ambiguity. As a consequence of this, mapping efforts often resort to a labor intensive manual mapping refinement through a human curator. Large Language Models (LLMs), such as the ones employed by ChatGPT, have generalizable abilities to perform a wide range of tasks, including question-answering and information extraction. Here we present MapperGPT, an approach that uses LLMs to review and refine mapping relationships as a post-processing step, in concert with existing high-recall methods that are based on lexical and structural heuristics. We evaluated MapperGPT on a series of alignment tasks from different domains, including anatomy, developmental biology, and renal diseases. We devised a collection of tasks that are designed to be particularly challenging for lexical methods. We show that when used in combination with high-recall methods, MapperGPT can provide a substantial improvement in accuracy, beating state-of-the-art (SOTA) methods such as LogMap.

翻译：对齐术语资源（包括本体、受控词汇表、分类法和值集）是医疗、化学和生物医学研究等多个领域数据整合的关键环节。实体映射是指确定这些资源中实体间对应关系的过程，例如基因标识符、疾病概念或化学实体标识符。已有多种工具基于共同结构特征和词汇信息（如标签和同义词）来计算此类映射。其中，词汇方法通常能提供较高的召回率，但由于词义歧义性，其精确率往往较低。因此，映射工作常需要人工策展人进行繁重的映射精化。大型语言模型（LLMs），如ChatGPT所采用的模型，具有可泛化的能力，能够执行包括问答和信息提取在内的广泛任务。在此，我们提出MapperGPT方法，该方法使用LLMs作为后处理步骤，对映射关系进行审查和精化，并与基于词汇和结构启发式的高召回现有方法协同工作。我们在来自不同领域（包括解剖学、发育生物学和肾脏疾病）的一系列对齐任务上评估了MapperGPT。我们设计了一组对词汇方法尤为具有挑战性的任务。结果表明，当与高召回方法联合使用时，MapperGPT能够显著提升准确率，超越LogMap等最新方法（SOTA）。