Abstract Biomedical ontologies such as the Unified Medical Language System (UMLS) play a crucial role in supporting practical applications such as predicting gene–disease associations, drug–drug interactions, and biomedical question answering. However, automatically maintaining and updating them remains a critical barrier as authoring high-quality textual definitions is both time-consuming and labor-intensive. As a result, UMLS ontology updates significantly lag behind the pace of scientific discovery, leaving many concepts without definitions or with outdated descriptions. Despite the pressing need for automated generation of UMLS concept definitions, this problem has largely remained unaddressed in prior research. To address this, we propose a novel diffusion-based approach for generating high-quality definitions of biomedical concepts. A key innovation in our approach is the adaptive semantic modulation mechanism, which dynamically adjusts the influence of semantic priors throughout the denoising process, enabling the model to flexibly balance local lexical fidelity with global semantic alignment. Extensive experiments on the largest available biomedical ontology dataset demonstrate that the proposed approach both significantly and consistently outperforms strong baseline algorithms across multiple evaluation metrics.
Dahal et al. (2026) studied this question.