Kavi Mahesh

39 papers receiving 292 citations

Peers

Kavi Mahesh
Comparison fields: 5 of 73
  • Artificial Intelligence 230
  • Human-Computer Interaction 64
  • Information Systems 55
  • Computer Vision and Pattern Recognition 46
  • Computer Networks and Communications 38
Replace Graham Wilcock with:
Graham Wilcock Finland
Abdelmajid Ben Hamadou Tunisia
Noriko Tomuro United States
Preetam Maloor United States
Kenji Kita Japan
James H. Coombs United States
David Stotts United States
Marlene Jones Canada
Mohsen Rashwan Egypt
Fumito Masui Japan
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Citations per field
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Citations per year

Countries citing papers authored by Kavi Mahesh

Since Specialization
Citations

This map shows the geographic impact of Kavi Mahesh'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 Kavi Mahesh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kavi Mahesh more than expected).

Fields of papers citing papers by Kavi Mahesh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kavi Mahesh. 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 Kavi Mahesh. The network helps show where Kavi Mahesh may publish in the future.

Co-authorship network of co-authors of Kavi Mahesh

This figure shows the co-authorship network connecting the top 25 collaborators of Kavi Mahesh. A scholar is included among the top collaborators of Kavi Mahesh 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 Kavi Mahesh. Kavi Mahesh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 1
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5
Digital Banking in Rural India – A StudY
1
6
A Computational Analysis of Mahabharata
3
7 10
8 1
9
Curating semantic linked open datasets for software engineering
1
10 3
11 5
12
Oracle at Trec8: A Lexical Approach.
11
13
Lexical Acquisition with WordNet and the Mikrokosmos Ontology
11
14
MINDS - Multi-lingual INteractive Document Summarization
5
15
Natural language processing for the World Wide Web : papers from the 1997 AAAI Symposium, March 24-26, Stanford, California
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16
Hunter-Gatherer: three search techniques integrated for natural language semantics
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17
Two principles and six techniques for rapid MT development
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18
Semantic Analysis in the Mikrokosmos Machine Translation Project
26
19 1
20
A Theory of Interaction and Independence in Sentence Understanding
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About Kavi Mahesh

Kavi Mahesh is a scholar working on Artificial Intelligence, Modeling and Simulation and Human-Computer Interaction, having authored 43 papers that have together received 383 indexed citations. Recurring topics across this work include Semantic Web and Ontologies (11 papers), Natural Language Processing Techniques (6 papers) and Topic Modeling (5 papers). The work is most often cited by research in Human-Computer Interaction (64 citations), Artificial Intelligence (230 citations) and Information Systems (55 citations). Kavi Mahesh has collaborated with scholars based in India, United States and Singapore. Frequent co-authors include Sergei Nirenburg, Stephen Beale, R. Jayashree, Paul R. Dixon, K. G. Smitha, A. P. Vinod, Rémi Zajac, Ashok K. Goel, Rinshu Dwivedi and Stephen Helmreich. Their work appears in journals such as Journal of Knowledge Management, International Journal of Human-Computer Studies and Journal of Information Science.

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.

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