Kuldeep Singh
- Artificial Intelligence top 10%
- Topic Modeling 14
- Natural Language Processing Techniques 11
- Semantic Web and Ontologies 10
- Advanced Graph Neural Networks 7
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- Data Quality and Management 2
- Information Systems top 10%
- Recommender Systems and Techniques 2
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- Biomedical Text Mining and Ontologies 3
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- Advanced Database Systems and Queries 2
- Co-authors
- Anant GuptaSaeedeh ShekarpourJens LehmannSören AuerMaría-Esther VidalFabrizio OrlandiMohamad Yaser JaradehAndreas Both
- Cited by
- Artificial IntelligenceGeography, Planning and DevelopmentManagement Science and Operations Research
- Journals
- SHILAP Revista de lepidopterología (1 paper)IEEE Access (1 paper)Knowledge and Information Systems (1 paper)
- Partner nations
- GermanyUnited StatesIndia
In The Last Decade
Kuldeep Singh
23 papers receiving 217 citations
Peers
Comparison fields: 5 of 38
- Artificial Intelligence 184
- Geography, Planning and Development 22
- Management Science and Operations Research 39
- Information Systems 55
- Transportation 11
Countries citing papers authored by Kuldeep Singh
This map shows the geographic impact of Kuldeep Singh'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 Kuldeep Singh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kuldeep Singh more than expected).
Fields of papers citing papers by Kuldeep Singh
This network shows the impact of papers produced by Kuldeep Singh. 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 Kuldeep Singh. The network helps show where Kuldeep Singh may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Kuldeep Singh, 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 | 0 | |
| 2 | 2024 | 0 | |
| 3 | 2023 | 2 | |
| 4 | 2023 | 15 | |
| 5 | 2022 | 11 | |
| 6 | 2022 | 1 | |
| 7 | 2022 | 2 | |
| 8 | 2021 | 15 | |
| 9 | 2021 | 2 | |
| 10 | 2021 | 5 | |
| 11 | 2020 | 3 | |
| 12 | Context-aware Entity Linking with Attentive Neural Networks on Wikidata Knowledge Graph | 2019 | 1 |
| 13 | 2019 | 38 | |
| 14 | 2018 | 30 | |
| 15 | 2018 | 33 | |
| 16 | 2017 | 17 | |
| 17 | 2017 | 9 | |
| 18 | 2016 | 9 | |
| 19 | The Behavior of Temperature on Insulated(MgZrO 3 ) Diesel Engine Piston With ANSYS | 2014 | 1 |
| 20 | 2013 | 8 |
About Kuldeep Singh
Kuldeep Singh is a scholar working on Artificial Intelligence, Geography, Planning and Development and Information Systems, having authored 25 papers that have together received 238 indexed citations. Recurring topics across this work include Topic Modeling (14 papers), Natural Language Processing Techniques (11 papers), Semantic Web and Ontologies (10 papers), Advanced Graph Neural Networks (7 papers), Biomedical Text Mining and Ontologies (3 papers), Advanced Database Systems and Queries (2 papers), Recommender Systems and Techniques (2 papers) and Data Quality and Management (2 papers). The work is most often cited by research in Artificial Intelligence (184 citations), Geography, Planning and Development (22 citations) and Management Science and Operations Research (39 citations). Kuldeep Singh has collaborated with scholars based in Germany, United States and India. Frequent co-authors include Anant Gupta, Saeedeh Shekarpour, Jens Lehmann, Sören Auer, María-Esther Vidal, Fabrizio Orlandi, Mohamad Yaser Jaradeh, Andreas Both, Christoph Lange and Johannes Hoffart. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Access and Knowledge and Information 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.