Taking advantage of the widespread use of ontologies to organise and
harmonize knowledge across several distinct domains, this paper proposes a
novel approach to improve an embedding-Large Language Model (embedding-LLM) of
interest by infusing the knowledge formalized by a reference ontology:
ontological knowledge infusion aims at boosting the ability of the considered
LLM to effectively model the knowledge domain described by the infused
ontology. The linguistic information (i.e. concept synonyms and descriptions)
and structural information (i.e. is-a relations) formalized by the ontology are
utilized to compile a comprehensive set of concept definitions, with the
assistance of a powerful generative LLM (i.e. GPT-3.5-turbo). These concept
definitions are then employed to fine-tune the target embedding-LLM using a
contrastive learning framework. To demonstrate and evaluate the proposed
approach, we utilize the biomedical disease ontology MONDO. The results show
that embedding-LLMs enhanced by ontological disease knowledge exhibit an
improved capability to effectively evaluate the similarity of in-domain
sentences from biomedical documents mentioning diseases, without compromising
their out-of-domain performance.

通过注入本体知识来改进嵌入式大语言模型（embedding-LLM），本研究利用广泛的本体使用和基于对照学习框架，通过医学疾病本体的生物医学文档，展示了提高嵌入式 LLM 在描述疾病领域中的相似性评估能力的实验结果。