Maya Ramanath
Impact in
- Signal Processing top 5%
- Data Management and Algorithms
- Artificial Intelligence top 2%
- Semantic Web and Ontologies
- Topic Modeling
- Natural Language Processing Techniques
- Advanced Graph Neural Networks
Papers in
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- Semantic Web and Ontologies 24
- Topic Modeling 15
- Natural Language Processing Techniques 9
- Advanced Graph Neural Networks 6
- Sentiment Analysis and Opinion Mining 5
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- Advanced Database Systems and Queries 18
- Co-authors
- Gerhard Weikum (23 shared papers)Gjergji Kasneci (7 shared papers)Fabian M. Suchanek (6 shared papers)Shady Elbassuoni (8 shared papers)Georgiana Ifrim (2 shared papers)Klaus Berberich (4 shared papers)Mohamed Yahya (4 shared papers)Volker Tresp (2 shared papers)
In The Last Decade
Maya Ramanath
50 papers receiving 789 citations
Peers
Comparison fields: 5 of 57
- Signal Processing 253
- Artificial Intelligence 634
- Computer Networks and Communications 284
- Information Systems 275
- Management Science and Operations Research 115
Countries citing papers authored by Maya Ramanath
This map shows the geographic impact of Maya Ramanath'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 Maya Ramanath with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maya Ramanath more than expected).
Fields of papers citing papers by Maya Ramanath
This network shows the impact of papers produced by Maya Ramanath. 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 Maya Ramanath. The network helps show where Maya Ramanath may publish in the future.
Co-authors
The 25 scholars most cited alongside Maya Ramanath, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 53 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2008 | 139 | |
| 2 | Natural Language Questions for the Web of Data | 2012 | 111 |
| 3 | 2009 | 77 | |
| 4 | 2002 | 72 | |
| 5 | 2009 | 60 | |
| 6 | 2009 | 52 | |
| 7 | 2009 | 39 | |
| 8 | Searching RDF Graphs with SPARQL and Keywords | 2010 | 29 |
| 9 | 2008 | 29 | |
| 10 | 2012 | 22 | |
| 11 | 1984 | 20 | |
| 12 | 2012 | 19 | |
| 13 | 2019 | 16 | |
| 14 | 2012 | 16 | |
| 15 | 2010 | 13 | |
| 16 | 2015 | 12 | |
| 17 | 1985 | 12 | |
| 18 | 2016 | 12 | |
| 19 | 2011 | 9 | |
| 20 | Personalizing the Search for Knowledge | 2008 | 9 |
About Maya Ramanath
Maya Ramanath is a scholar working on Artificial Intelligence, Computer Networks and Communications, Signal Processing, Information Systems and Computer Vision and Pattern Recognition, having authored 53 papers that have together received 871 indexed citations. Recurring topics across this work include Semantic Web and Ontologies (24 papers), Advanced Database Systems and Queries (18 papers), Data Management and Algorithms (15 papers), Topic Modeling (15 papers), Natural Language Processing Techniques (9 papers), Web Data Mining and Analysis (7 papers), Advanced Graph Neural Networks (6 papers) and Sentiment Analysis and Opinion Mining (5 papers). The work is most often cited by research in Signal Processing (253 citations), Artificial Intelligence (634 citations), Computer Networks and Communications (284 citations), Information Systems (275 citations) and Management Science and Operations Research (115 citations). Maya Ramanath has collaborated with scholars based in Germany, India and Canada. Frequent co-authors include Gerhard Weikum, Gjergji Kasneci, Fabian M. Suchanek, Shady Elbassuoni, Georgiana Ifrim, Klaus Berberich, Mohamed Yahya, Volker Tresp, Jayant R. Haritsa and Juliana Freire. Their work appears in journals such as Proceedings of the VLDB Endowment, Journal of Graph Theory, Combustion and Flame, Communications of the ACM and Pattern Recognition.
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.