Ontology extension by online clustering with large language model agents.

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Frontiers in Big Data Pub Date : 2024-10-07 eCollection Date: 2024-01-01 DOI:10.3389/fdata.2024.1463543
Guanchen Wu, Chen Ling, Ilana Graetz, Liang Zhao
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Abstract

An ontology is a structured framework that categorizes entities, concepts, and relationships within a domain to facilitate shared understanding, and it is important in computational linguistics and knowledge representation. In this paper, we propose a novel framework to automatically extend an existing ontology from streaming data in a zero-shot manner. Specifically, the zero-shot ontology extension framework uses online and hierarchical clustering to integrate new knowledge into existing ontologies without substantial annotated data or domain-specific expertise. Focusing on the medical field, this approach leverages Large Language Models (LLMs) for two key tasks: Symptom Typing and Symptom Taxonomy among breast and bladder cancer survivors. Symptom Typing involves identifying and classifying medical symptoms from unstructured online patient forum data, while Symptom Taxonomy organizes and integrates these symptoms into an existing ontology. The combined use of online and hierarchical clustering enables real-time and structured categorization and integration of symptoms. The dual-phase model employs multiple LLMs to ensure accurate classification and seamless integration of new symptoms with minimal human oversight. The paper details the framework's development, experiments, quantitative analyses, and data visualizations, demonstrating its effectiveness in enhancing medical ontologies and advancing knowledge-based systems in healthcare.

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利用大型语言模型代理进行在线聚类的本体扩展。
本体是一种结构化框架,它将某一领域内的实体、概念和关系进行分类,以促进共同理解,在计算语言学和知识表示中非常重要。在本文中,我们提出了一个新颖的框架,以零镜头的方式从流式数据中自动扩展现有的本体。具体来说,零镜头本体扩展框架使用在线和分层聚类将新知识整合到现有本体中,而无需大量注释数据或特定领域的专业知识。该方法以医疗领域为重点,利用大型语言模型(LLM)完成两项关键任务:乳腺癌和膀胱癌幸存者的症状分类和症状分类学。症状分类包括从非结构化的在线患者论坛数据中识别医学症状并对其进行分类,而症状分类则是将这些症状组织并整合到现有的本体论中。在线聚类和分层聚类的结合使用实现了症状的实时、结构化分类和整合。双阶段模型采用了多个 LLM,以确保分类的准确性和新症状的无缝整合,并尽量减少人为监督。论文详细介绍了该框架的开发、实验、定量分析和数据可视化,展示了其在增强医学本体论和推进医疗保健领域基于知识的系统方面的有效性。
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来源期刊
CiteScore
5.20
自引率
3.20%
发文量
122
审稿时长
13 weeks
期刊最新文献
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