Biomedical Relation Extraction Using LLMs and Knowledge Graphs
Publication Date
1-1-2024
Document Type
Conference Proceeding
Publication Title
Proceedings - IEEE 10th International Conference on Big Data Computing Service and Applications, BigDataService 2024
DOI
10.1109/BigDataService62917.2024.00015
First Page
60
Last Page
69
Abstract
Due to the rapid growth of research papers on biomedical topics, it has become increasingly important to make advancements in biomedical Natural Language Processing (NLP). Biomedical NLP enables us to extract important information from text, such as new insights into the role of different genes in disease susceptibility, or the potential for drug therapies that are effective against one disease to work effectively against another. In this paper, we present a comparative evaluation of the binary relation classification capabilities of the current state-of-the-art binary relation classifier, BioBERT, against recently released open-source large language models, Gemma-2b, Gemma-7b, and Llama2-7b, which we fine-tune with the benchmark GAD and EU-ADR datasets. In addition, we quantify the potential of discovering new relationships by utilizing knowledge graphs built out of known binary relations.
Keywords
biomedical natural language processing, LLMs, relation extraction
Department
Computer Engineering
Recommended Citation
Pranav Chellagurki, Sai Prasanna Kumar Kumaru, Rahul Raghava Peela, Neeharika Yeluri, Carlos Rojas, and Jorjeta Jetcheva. "Biomedical Relation Extraction Using LLMs and Knowledge Graphs" Proceedings - IEEE 10th International Conference on Big Data Computing Service and Applications, BigDataService 2024 (2024): 60-69. https://doi.org/10.1109/BigDataService62917.2024.00015
