Kee Siong Ng
- Artificial Intelligence top 10%
- Computer Networks and Communications top 10%
- Information Systems top 10%
- Signal Processing top 10%
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- Arun KumarJoseph M. HellersteinFlorian SchoppmannKun LiDaisy Zhe WangJohn W. LloydMarcus HütterJoel Veness
- Topics
- Logic, Reasoning, and Knowledge (4 papers)Bayesian Modeling and Causal Inference (3 papers)AI-based Problem Solving and Planning (3 papers)
In The Last Decade
Kee Siong Ng
14 papers receiving 295 citations
Peers
Comparison fields: 5 of 43
- Artificial Intelligence 192
- Computer Networks and Communications 124
- Information Systems 104
- Signal Processing 87
- Computer Vision and Pattern Recognition 60
Countries citing papers authored by Kee Siong Ng
This map shows the geographic impact of Kee Siong Ng'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 Kee Siong Ng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kee Siong Ng more than expected).
Fields of papers citing papers by Kee Siong Ng
This network shows the impact of papers produced by Kee Siong Ng. 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 Kee Siong Ng. The network helps show where Kee Siong Ng may publish in the future.
Co-authorship network of co-authors of Kee Siong Ng
This figure shows the co-authorship network connecting the top 25 collaborators of Kee Siong Ng. A scholar is included among the top collaborators of Kee Siong Ng 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 Kee Siong Ng. Kee Siong Ng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 10 | |
| 2 | 7 | |
| 3 | Private Digital Identity on Blockchain. | 5 |
| 4 | 2 | |
| 5 | 1 | |
| 6 | 15 | |
| 7 | 232 | |
| 8 | 0 | |
| 9 | 2 | |
| 10 | 8 | |
| 11 | 12 | |
| 12 | 3 | |
| 13 | 10 | |
| 14 | 8 | |
| 15 | Alkemy: A Learning System based on an Expressive Knowledge Representation Formalism | 1 |
| 16 | Symbolic Learning for Adaptive Agents | 6 |
About Kee Siong Ng
Kee Siong Ng is a scholar working on Artificial Intelligence, History and Philosophy of Science and Health Information Management, having authored 16 papers that have together received 322 indexed citations. Recurring topics across this work include Logic, Reasoning, and Knowledge (4 papers), Bayesian Modeling and Causal Inference (3 papers) and AI-based Problem Solving and Planning (3 papers). The work is most often cited by research in Signal Processing (87 citations), Artificial Intelligence (192 citations) and Computer Networks and Communications (124 citations). Kee Siong Ng has collaborated with scholars based in Australia, Canada and Singapore. Frequent co-authors include Arun Kumar, Joseph M. Hellerstein, Florian Schoppmann, Kun Li, Daisy Zhe Wang, John W. Lloyd, Marcus Hütter, Joel Veness, William Uther and Michael Bowling. Their work appears in journals such as ACM Computing Surveys, Proceedings of the VLDB Endowment and Autonomous Agents and Multi-Agent Systems.
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.