  Ontologies play a crucial role in organizing and representing knowledge.
However, even current ontologies do not encompass all relevant concepts and
relationships. Here, we explore the potential of large language models (LLM) to
expand an existing ontology in a semi-automated fashion. We demonstrate our
approach on the biomedical ontology SNOMED-CT utilizing semantic relation types
from the widely used UMLS semantic network. We propose a method that uses
conversational interactions with an LLM to analyze clinical practice guidelines
(CPGs) and detect the relationships among the new medical concepts that are not
present in SNOMED-CT. Our initial experimentation with the conversational
prompts yielded promising preliminary results given a manually generated gold
standard, directing our future potential improvements.

Computation and Language;Artificial Intelligence