John Cabral
Impact in
- Artificial Intelligence top 5%
- Semantic Web and Ontologies
- Natural Language Processing Techniques
- Topic Modeling
- Advanced Text Analysis Techniques
- AI-based Problem Solving and Planning
- Information Systems top 10%
- Service-Oriented Architecture and Web Services
Papers in
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- Natural Language Processing Techniques 6
- Semantic Web and Ontologies 5
- Topic Modeling 4
- Algorithms and Data Compression 1
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- Distributed systems and fault tolerance 1
- Distributed and Parallel Computing Systems 1
- Co-authors
- Michael Witbrock (5 shared papers)Cynthia Matuszek (4 shared papers)David Baxter (4 shared papers)Jon Curtis (3 shared papers)Doug Lenat (1 shared paper)David Schneider (2 shared papers)Douglas B. Lenat (1 shared paper)Peter J. Wagner (1 shared paper)
- Journals
- The Florida AI Research Society (1 paper)National Conference on Artificial Intelligence (1 paper)Maryland Shared Open Access Repository (USMAI Consortium) (4 papers)
In The Last Decade
John Cabral
7 papers receiving 285 citations
Peers
Comparison fields: 5 of 42
- Artificial Intelligence 302
- Information Systems 91
- Management Science and Operations Research 33
- Signal Processing 19
- Geography, Planning and Development 9
Countries citing papers authored by John Cabral
This map shows the geographic impact of John Cabral'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 John Cabral with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Cabral more than expected).
Fields of papers citing papers by John Cabral
This network shows the impact of papers produced by John Cabral. 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 John Cabral. The network helps show where John Cabral may publish in the future.
Co-authors
The 10 scholars most cited alongside John Cabral, 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 | 2006 | 212 | |
| 2 | 2005 | 73 | |
| 3 | 2006 | 23 | |
| 4 | 2006 | 21 | |
| 5 | Methods of Rule Acquisition in the TextLearner System. | 2009 | 5 |
| 6 | 2005 | 5 | |
| 7 | 2006 | 1 |
About John Cabral
John Cabral is a scholar working on Artificial Intelligence, Computer Networks and Communications, Information Systems, Hardware and Architecture and Infectious Diseases, having authored 7 papers that have together received 340 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (6 papers), Semantic Web and Ontologies (5 papers), Topic Modeling (4 papers), Parallel Computing and Optimization Techniques (1 paper), Distributed systems and fault tolerance (1 paper), Distributed and Parallel Computing Systems (1 paper), Algorithms and Data Compression (1 paper) and Service-Oriented Architecture and Web Services (1 paper). The work is most often cited by research in Artificial Intelligence (302 citations), Information Systems (91 citations), Management Science and Operations Research (33 citations), Signal Processing (19 citations) and Geography, Planning and Development (9 citations). John Cabral has collaborated with scholars based in Australia and Brazil. Frequent co-authors include Michael Witbrock, Cynthia Matuszek, David Baxter, Jon Curtis, Doug Lenat, David Schneider, Douglas B. Lenat, Peter J. Wagner, Jorge Figueiredo and Dalton Guerrero. Their work appears in journals such as The Florida AI Research Society, National Conference on Artificial Intelligence and Maryland Shared Open Access Repository (USMAI Consortium).
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.