AI-driven knowledge-data graph for tracking global arbovirus research
Arboviruses represent a critical area of research in the field of infectious diseases, necessitating systematic and comprehensive analysis to extract actionable insights. This study introduces an AI-driven knowledge-data graph (KDG) that provides novel perspectives on the global arbovirus research landscape. The KDG comprehensively covers the meaningful research directions and their fine-grained subdivisions organized in a hierarchical structure. We have involved human experts in interacting with a generic large language model (LLM) for constructing the hierarchical topic structure of the overall arbovirus research, forming the basis of KDG for AI-driven meta-analysis of the research area. We then pre-trained a domain-specific language model, ArboBERT, specifically tailored for arbovirus, to establish the knowledge-data association in a low-resource, high-efficiency, and high-accuracy fashion to enable the integration of the structural and statistical analysis. We further extracted key entities such as researchers, institutes, journals, geographical entities, time, and virus species to form KDG at a deeper level of the topic hierarchy, allowing for more nuanced insights and a better understanding of the complex relationships within the field. This AI-driven KDG enables perspectives from people-related, temporal, geographical, and interactive views, facilitating dynamic, comprehensive, and in-depth analysis and insights into global arbovirus research.
Authors
- Guanlin Chen (ORCID: https://orcid.org/0000-0002-4255-658X)
- Wan‐Yuo Guo (ORCID: https://orcid.org/0000-0003-2640-8664)
- Yongxia Shi (ORCID: https://orcid.org/0000-0003-3590-2365)
- Zhaoyan Ming (ORCID: https://orcid.org/0000-0002-6766-4579)
- Han Xia (ORCID: https://orcid.org/0000-0001-9932-6040)
- Kui Su (ORCID: https://orcid.org/0000-0002-6304-9907)
- Sheng Tang (ORCID: https://orcid.org/0000-0003-3573-2407)
- Chenglong Hu (ORCID: https://orcid.org/0000-0003-1554-269X)
- Jicheng Huang (ORCID: https://orcid.org/0000-0002-5882-7559)
- Zhiming Yuan (ORCID: https://orcid.org/0000-0002-3234-9616)
- Zhaolin Li (ORCID: https://orcid.org/0000-0002-5980-0315)
- X.F. Li (ORCID: https://orcid.org/0009-0002-0011-7906)
- Ying Huang (ORCID: https://orcid.org/0000-0001-6411-497X)
- J. C. Peng (ORCID: https://orcid.org/0009-0000-7782-230X)
- Weijun Sun
- Xiangyu Huangfu
Institutions
- Yangtze University (CN)
- Jilin University (CN)
- University of California San Diego (US)
- Shanghai Shipbuilding Technology Research Institute (CN)
- Shanghai Customs College (CN)
- Wuhan Institute of Virology (CN)
- Zhejiang Lab (CN)
- Jilin Province Science and Technology Department (CN)
- University of Chinese Academy of Sciences (CN)
- Hangzhou City University
- City University of Macau (MO)
- Hubei University (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- Journal Of Big Data
- Published
- 2025-09-24
- DOI
- https://doi.org/10.1186/s40537-025-01256-w
- Primary Topic
- Mosquito-borne diseases and control
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China
- Natural Science Foundation of Zhejiang Province