Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/60665, first published .
An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontology-Enhanced Large Language Models: Development Study

An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontology-Enhanced Large Language Models: Development Study

An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontology-Enhanced Large Language Models: Development Study

Authors of this article:

Lang Cao1 Author Orcid Image ;   Jimeng Sun1 Author Orcid Image ;   Adam Cross2 Author Orcid Image

Journals

  1. Canpolat C. Leveraging Knowledge Graphs for Enhanced Medical Reasoning in Personalized Medicine for Rare Diseases. Next Frontier For Life Sciences and AI 2024;8(1):77 View
  2. Li Z, Fu Q, Zhao Y, Ren W. Empowering standardized residency training in China through large language models: problem analysis and solutions. Annals of Medicine 2025;57(1) View
  3. Kang H, Li J, Hou L, Xu X, Zheng S, Li Q. Large Language Model–Enhanced Drug Repositioning Knowledge Extraction via Long Chain-of-Thought: Development and Evaluation Study. JMIR Medical Informatics 2025;13:e77837 View

Conference Proceedings

  1. Cui J. Proceedings of the 2025 International Conference on Management Science and Computer Engineering. Empowering Digital Transformation of Traditional Chinese Medicine with Knowledge Graph-Enhanced Large Language Models: Opportunities and Challenges View
  2. Wang M, Li H, Xie J, Zhang M, Wang Y, Zhang Z. 2025 10th International Conference on Machine Learning Technologies (ICMLT). Enhanced Reverse Reasoning in LLMs for Further Research Directions Generation View