Can Large Language Models Augment a Biomedical Ontology with missing Concepts and Relations?

• URL: http://arxiv.org/abs/2311.06858v1
• Date: Sun, 12 Nov 2023 14:20:55 GMT
• Title: Can Large Language Models Augment a Biomedical Ontology with missing Concepts and Relations?
• Authors: Antonio Zaitoun, Tomer Sagi, Szymon Wilk, Mor Peleg
• Abstract summary: We propose a method that uses semantic interactions with an LLM to analyze clinical practice guidelines. Our initial experimentation with the prompts yielded promising results given a manually generated gold standard.
• Score: 1.1060425537315088
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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