Donghong Ji
- Artificial Intelligence top 1%
- Topic Modeling 68
- Natural Language Processing Techniques 52
- Advanced Text Analysis Techniques 23
- Advanced Graph Neural Networks 6
- Speech and dialogue systems 4
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- Data Quality and Management 6
- Health Informatics top 10%
- Information Systems top 5%
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- Biomedical Text Mining and Ontologies 21
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- Multimodal Machine Learning Applications 6
- Partner nations
- ChinaUnited StatesSingapore
In The Last Decade
Donghong Ji
84 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 103
- Artificial Intelligence 1.3k
- Management Science and Operations Research 161
- Health Informatics 13
- Health Information Management 43
- Information Systems 153
Countries citing papers authored by Donghong Ji
This map shows the geographic impact of Donghong Ji'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 Donghong Ji with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Donghong Ji more than expected).
Fields of papers citing papers by Donghong Ji
This network shows the impact of papers produced by Donghong Ji. 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 Donghong Ji. The network helps show where Donghong Ji may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Donghong Ji, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 2 | |
| 2 | 2024 | 0 | |
| 3 | 2023 | 5 | |
| 4 | 2023 | 6 | |
| 5 | 2022 | 17 | |
| 6 | 2022 | 1 | |
| 7 | 2021 | 64 | |
| 8 | 2021 | 12 | |
| 9 | 2021 | 43 | |
| 10 | 2020 | 2 | |
| 11 | 2020 | 7 | |
| 12 | 2020 | 15 | |
| 13 | 2017 | 15 | |
| 14 | 2015 | 5 | |
| 15 | Multi-Document Summarization Based on Event Term Semantic Relation Graph Clustering | 2010 | 0 |
| 16 | Query-Focused Multi-Document Summarization Using Co-Training Based Semi-Supervised Learning | 2009 | 1 |
| 17 | Document Re-ranking via Wikipedia Articles for Definition/Biography Type Questions | 2009 | 1 |
| 18 | Sentence Ordering based on Cluster Adjacency in Multi-Document Summarization | 2008 | 6 |
| 19 | Overview of the NTCIR-7 ACLIA IR4QA Task | 2008 | 22 |
| 20 | I2R at NTCIR5. | 2005 | 1 |
About Donghong Ji
Donghong Ji is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computer Vision and Pattern Recognition, having authored 88 papers that have together received 1.5k indexed citations. Recurring topics across this work include Topic Modeling (68 papers), Natural Language Processing Techniques (52 papers), Advanced Text Analysis Techniques (23 papers), Biomedical Text Mining and Ontologies (21 papers), Multimodal Machine Learning Applications (6 papers), Advanced Graph Neural Networks (6 papers), Data Quality and Management (6 papers) and Speech and dialogue systems (4 papers). The work is most often cited by research in Artificial Intelligence (1.3k citations), Management Science and Operations Research (161 citations) and Health Informatics (13 citations). Donghong Ji has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Yafeng Ren, Hao Fei, Fei Li, Meishan Zhang, Jingye Li, Fei Hao, Chong Teng, Shengqiong Wu, Yue Zhang and Xiaohui Liang. Their work appears in journals such as Bioinformatics, PLoS ONE and IEEE Access.
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.