  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.
