CoRTEx：通过解释进行术语表示的对比学习及其在生物医学知识图谱构建中的应用
CoRTEx: Contrastive Learning for Representing Terms via Explanations with Applications on Constructing Biomedical Knowledge Graphs
AI总结：
本研究提出CoRTEx模型，利用大语言模型生成的术语解释结合对比学习增强术语表示，搭配ChatGPT辅助的BIRCH算法，实现了更优的生物医学术语聚类效果，可用于大规模生物医学知识图谱构建。
摘要：
研究目的：生物医学知识图谱在各类生物医学研究领域中发挥着核心作用。与此同时，术语聚类作为构建这些知识图谱的关键步骤，目标是识别同义术语。由于知识储备不足，此前基于统一医学语言系统（Unified Medical Language System, UMLS）同义词训练的对比学习模型在聚类疑难术语时表现不佳，且在UMLS术语之外的泛化能力较差。本研究利用大语言模型（Large Language Models, LLMs）的世界知识，提出“通过解释进行术语表示的对比学习（Contrastive Learning for Representing Terms via Explanations, CoRTEx）”方法，以增强术语表示能力并显著提升术语聚类效果。\n材料与方法：模型训练环节包括使用ChatGPT为清洗后的UMLS术语子集生成解释。我们采用对比学习方法，同时考虑术语与解释的嵌入表示，并逐步引入难负样本。此外，我们设计了一种ChatGPT辅助的BIRCH算法，用于对新本体进行高效聚类。\n研究结果：我们构建了一个聚类测试集和一个难负样本测试集，本研究提出的模型在两个测试集上均持续取得最高的F1分数。借助CoRTEx嵌入和改进后的BIRCH算法，我们将生物医学信息学本体系统（Biomedical Informatics Ontology System, BIOS）中的35580932个术语划分为22104559个聚类，仅需向ChatGPT发送O(N)次查询。案例研究表明，得益于解释信息的辅助，该模型在处理疑难样本时具有出色的效能。\n结论：通过将术语与其解释对齐，CoRTEx在准确率上优于基准模型，且在训练集之外仍具备鲁棒性，适用于大规模生物医学本体的术语聚类任务。
英文摘要：
Objective: Biomedical Knowledge Graphs play a pivotal role in various biomedical research domains. Concurrently, term clustering emerges as a crucial step in constructing these knowledge graphs, aiming to identify synonymous terms. Due to a lack of knowledge, previous contrastive learning models trained with Unified Medical Language System (UMLS) synonyms struggle at clustering difficult terms and do not generalize well beyond UMLS terms. In this work, we leverage the world knowledge from Large Language Models (LLMs) and propose Contrastive Learning for Representing Terms via Explanations (CoRTEx) to enhance term representation and significantly improves term clustering. Materials and Methods: The model training involves generating explanations for a cleaned subset of UMLS terms using ChatGPT. We employ contrastive learning, considering term and explanation embeddings simultaneously, and progressively introduce hard negative samples. Additionally, a ChatGPT-assisted BIRCH algorithm is designed for efficient clustering of a new ontology. Results: We established a clustering test set and a hard negative test set, where our model consistently achieves the highest F1 score. With CoRTEx embeddings and the modified BIRCH algorithm, we grouped 35,580,932 terms from the Biomedical Informatics Ontology System (BIOS) into 22,104,559 clusters with O(N) queries to ChatGPT. Case studies highlight the model's efficacy in handling challenging samples, aided by information from explanations. Conclusion: By aligning terms to their explanations, CoRTEx demonstrates superior accuracy over benchmark models and robustness beyond its training set, and it is suitable for clustering terms for large-scale biomedical ontologies.
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