CoRTEx: contrastive learning for representing terms via explanations with applications on constructing biomedical knowledge graphs.

Autor:	Ying H; Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing, 100084, China., Zhao Z; Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing, 100084, China., Zhao Y; Weiyang College, Tsinghua University, Beijing, 100084, China., Zeng S; Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, United States., Yu S; Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing, 100084, China.
Jazyk:	angličtina
Zdroj:	Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2024 Sep 01; Vol. 31 (9), pp. 1912-1920.
DOI:	10.1093/jamia/ocae115
Abstrakt:	Objectives: 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. (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)
Databáze:	MEDLINE
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