Ai Kawazoe
- Epidemiology
- Artificial Intelligence top 10%
- Molecular Biology
- Sociology and Political Science
- Public Health, Environmental and Occupational Health
- Co-authors
- Nigel CollierSon DoanMike ConwayMika ShigematsuKoichi TakeuchiAsanee KawtrakulHung Q. NgoKiyosu Taniguchi
- Topics
- Biomedical Text Mining and Ontologies (14 papers)Data-Driven Disease Surveillance (8 papers)Semantic Web and Ontologies (7 papers)
In The Last Decade
Ai Kawazoe
17 papers receiving 425 citations
Peers
Comparison fields: 5 of 100
- Epidemiology 206
- Artificial Intelligence 171
- Molecular Biology 165
- Sociology and Political Science 74
- Public Health, Environmental and Occupational Health 62
Countries citing papers authored by Ai Kawazoe
This map shows the geographic impact of Ai Kawazoe's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ai Kawazoe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ai Kawazoe more than expected).
Fields of papers citing papers by Ai Kawazoe
This network shows the impact of papers produced by Ai Kawazoe. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ai Kawazoe. The network helps show where Ai Kawazoe may publish in the future.
Co-authorship network of co-authors of Ai Kawazoe
This figure shows the co-authorship network connecting the top 25 collaborators of Ai Kawazoe. A scholar is included among the top collaborators of Ai Kawazoe based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ai Kawazoe. Ai Kawazoe is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | Overview of Todai Robot Project and Evaluation Framework of its NLP-based Problem Solving | 18 |
| 3 | University Entrance Examinations as a Benchmark Resource for NLP-based Problem Solving | 5 |
| 4 | 7 | |
| 5 | 80 | |
| 6 | 9 | |
| 7 | An ontology-driven system for detecting global health events | 15 |
| 8 | 43 | |
| 9 | 2 | |
| 10 | 12 | |
| 11 | 15 | |
| 12 | Classifying disease outbreak reports using n-grams and semantic features | 5 |
| 13 | 170 | |
| 14 | 39 | |
| 15 | 6 | |
| 16 | The Development of a Schema for the Annotation of Terms in the Biocaster Disease Detecting/Tracking System. | 12 |
| 17 | 3 | |
| 18 | 0 |
About Ai Kawazoe
Ai Kawazoe is a scholar working on Artificial Intelligence, Communication and Epidemiology, having authored 18 papers that have together received 447 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (14 papers), Data-Driven Disease Surveillance (8 papers) and Semantic Web and Ontologies (7 papers). The work is most often cited by research in Epidemiology (206 citations), Artificial Intelligence (171 citations) and Modeling and Simulation (22 citations). Ai Kawazoe has collaborated with scholars based in Japan, Vietnam and Australia. Frequent co-authors include Nigel Collier, Son Doan, Mike Conway, Mika Shigematsu, Koichi Takeuchi, Asanee Kawtrakul, Hung Q. Ngo, Kiyosu Taniguchi, Yoshio Tateno and Lihua Jin. Their work appears in journals such as Bioinformatics, BMC Bioinformatics and Journal of Medical Internet Research.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.