Jin-Ge Yao
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Biomedical Engineering
- Information Systems
- Management Science and Operations Research
- Topics
- Topic Modeling (18 papers)Natural Language Processing Techniques (18 papers)Advanced Text Analysis Techniques (7 papers)
- Journals
- Knowledge and Information SystemsJournal of Web SemanticsInternational Joint Conference on Natural Language Processing
- Partner nations
- ChinaUnited KingdomUnited States
In The Last Decade
Jin-Ge Yao
22 papers receiving 527 citations
Peers
Comparison fields: 5 of 54
- Artificial Intelligence 398
- Computer Vision and Pattern Recognition 211
- Biomedical Engineering 57
- Information Systems 42
- Management Science and Operations Research 27
Countries citing papers authored by Jin-Ge Yao
This map shows the geographic impact of Jin-Ge Yao'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 Jin-Ge Yao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jin-Ge Yao more than expected).
Fields of papers citing papers by Jin-Ge Yao
This network shows the impact of papers produced by Jin-Ge Yao. 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 Jin-Ge Yao. The network helps show where Jin-Ge Yao may publish in the future.
Co-authorship network of co-authors of Jin-Ge Yao
This figure shows the co-authorship network connecting the top 25 collaborators of Jin-Ge Yao. A scholar is included among the top collaborators of Jin-Ge Yao 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 Jin-Ge Yao. Jin-Ge Yao 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 | 21 | |
| 3 | 13 | |
| 4 | LinkingPark: An Integrated Approach for Semantic Table Interpretation. | 5 |
| 5 | 40 | |
| 6 | 54 | |
| 7 | 0 | |
| 8 | 10 | |
| 9 | 19 | |
| 10 | 28 | |
| 11 | 18 | |
| 12 | 11 | |
| 13 | Leveraging Diverse Lexical Chains to Construct Essays for Chinese College Entrance Examination | 2 |
| 14 | 3 | |
| 15 | 86 | |
| 16 | 1 | |
| 17 | 11 | |
| 18 | Compressive document summarization via sparse optimization | 23 |
| 19 | 41 | |
| 20 | 2 |
About Jin-Ge Yao
Jin-Ge Yao is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and General Social Sciences, having authored 24 papers that have together received 564 indexed citations. Recurring topics across this work include Topic Modeling (18 papers), Natural Language Processing Techniques (18 papers) and Advanced Text Analysis Techniques (7 papers). The work is most often cited by research in Artificial Intelligence (398 citations), Computer Vision and Pattern Recognition (211 citations) and General Social Sciences (14 citations). Jin-Ge Yao has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Xiaojun Wan, Jianguo Xiao, Chin-Yew Lin, Lang Huang, Jianyuan Guo, Kai Han, Chao Zhang, Yuhui Yuan, Jinpeng Wang and Rong Pan. Their work appears in journals such as Knowledge and Information Systems, Journal of Web Semantics and International Joint Conference on Natural Language Processing.
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.