MapperGPT：用于实体关联与映射的大语言模型
MapperGPT: Large Language Models for Linking and Mapping Entities
AI总结：
针对实体映射中词汇方法精确率低、需人工优化的问题，提出 MapperGPT 方法，结合大语言模型与高召回启发式方法优化映射，在多领域任务上准确率超越现有最优方法。
摘要：
对齐本体、受控词表、分类法和值集等术语资源，是医疗、化学、生物医学研究等众多领域中数据集成的关键环节。实体映射是确定这些资源中实体（如基因标识符、疾病概念或化学实体标识符）之间对应关系的过程。目前已有许多工具基于共同结构特征以及标签、同义词等词汇信息来计算此类映射。但由于词汇歧义的存在，尤其是词汇类方法通常召回率很高，精确率却很低。因此，映射工作往往需要人工策展员进行耗时耗力的手动映射优化。\nChatGPT 所采用的大语言模型（LLM）具备执行问答、信息抽取等各类任务的通用能力。本文提出 MapperGPT 方法，该方法将大语言模型作为后处理步骤，与现有的基于词汇和结构启发式的高召回率方法协同，用于审查和优化映射关系。\n我们在来自解剖学、发育生物学、肾脏疾病等不同领域的一系列对齐任务上评估了 MapperGPT，还设计了一组对词汇方法而言极具挑战性的任务。结果表明，与高召回率方法结合使用时，MapperGPT 能大幅提升准确率，性能优于 LogMap 等当前最优（SOTA）方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
