HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting

• URL: http://arxiv.org/abs/2304.05973v1
• Date: Wed, 12 Apr 2023 16:54:26 GMT
• Title: HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting
• Authors: Jiaying Lu, Jiaming Shen, Bo Xiong, Wenjing Ma, Steffen Staab, Carl Yang
• Abstract summary: HiPrompt is a supervision-efficient knowledge fusion framework. It elicits the few-shot reasoning ability of large language models through hierarchy-oriented prompts. Empirical results on the collected KG-Hi-BKF benchmark datasets demonstrate the effectiveness of HiPrompt.
• Score: 33.1455954220194
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Medical decision-making processes can be enhanced by comprehensive biomedical knowledge bases, which require fusing knowledge graphs constructed from different sources via a uniform index system. The index system often organizes biomedical terms in a hierarchy to provide the aligned entities with fine-grained granularity. To address the challenge of scarce supervision in the biomedical knowledge fusion (BKF) task, researchers have proposed various unsupervised methods. However, these methods heavily rely on ad-hoc lexical and structural matching algorithms, which fail to capture the rich semantics conveyed by biomedical entities and terms. Recently, neural embedding models have proved effective in semantic-rich tasks, but they rely on sufficient labeled data to be adequately trained. To bridge the gap between the scarce-labeled BKF and neural embedding models, we propose HiPrompt, a supervision-efficient knowledge fusion framework that elicits the few-shot reasoning ability of large language models through hierarchy-oriented prompts. Empirical results on the collected KG-Hi-BKF benchmark datasets demonstrate the effectiveness of HiPrompt.

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