Can Large Language Models Augment a Biomedical Ontology with missing Concepts and Relations?
This work addresses the challenge of maintaining comprehensive biomedical ontologies for healthcare professionals and researchers, though it is incremental as it builds on existing LLM capabilities.
The researchers tackled the problem of incomplete biomedical ontologies by using large language models to semi-automatically expand SNOMED-CT with missing concepts and relations from clinical practice guidelines, yielding promising preliminary results against a manually generated gold standard.
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.