Clematide
Impact in
- Artificial Intelligence top 2%
- Natural Language Processing Techniques
- Topic Modeling
- Semantic Web and Ontologies
- Advanced Text Analysis Techniques
- Speech Recognition and Synthesis
Papers in
-
- Natural Language Processing Techniques 56
- Topic Modeling 44
- Semantic Web and Ontologies 25
- Speech and dialogue systems 6
- Co-authors
- Peter Makarov (12 shared papers)Fabio Rinaldi (28 shared papers)Martin Volk (14 shared papers)Maud Ehrmann (10 shared papers)Manfred Klenner (11 shared papers)Gerold Schneider (12 shared papers)Martin Romacker (5 shared papers)Matteo Romanello (7 shared papers)
- Journals
- Database (4 papers)Language Resources and Evaluation (2 papers)BMC Bioinformatics (2 papers)Journal of Biomedical Semantics (1 paper)European Sociological Review (1 paper)
- Partner nations
- SwitzerlandFranceGermany
In The Last Decade
Clematide
97 papers receiving 833 citations
Peers
Comparison fields: 5 of 86
- Artificial Intelligence 672
- Health Informatics 9
- Computer Vision and Pattern Recognition 128
- Molecular Biology 321
- Information Systems 80
Countries citing papers authored by Clematide
This map shows the geographic impact of Clematide'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 Clematide with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Clematide more than expected).
Fields of papers citing papers by Clematide
This network shows the impact of papers produced by Clematide. 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 Clematide. The network helps show where Clematide may publish in the future.
Co-authors
The 25 scholars most cited alongside Clematide, 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 106 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Querying the Semantic Web with Ginseng: A Guided Input Natural Language Search Engine | 2009 | 59 |
| 2 | 2010 | 51 | |
| 3 | 2008 | 38 | |
| 4 | 2015 | 38 | |
| 5 | 2017 | 36 | |
| 6 | 2020 | 35 | |
| 7 | 2018 | 30 | |
| 8 | 2001 | 27 | |
| 9 | 2020 | 24 | |
| 10 | 2012 | 23 | |
| 11 | 2018 | 23 | |
| 12 | 2018 | 23 | |
| 13 | 2014 | 22 | |
| 14 | 2016 | 21 | |
| 15 | 2012 | 21 | |
| 16 | 2012 | 19 | |
| 17 | 2018 | 19 | |
| 18 | 2013 | 16 | |
| 19 | 2011 | 15 | |
| 20 | 2013 | 15 |
About Clematide
Clematide is a scholar working on Artificial Intelligence, Language and Linguistics, Molecular Biology, Computer Vision and Pattern Recognition and General Social Sciences, having authored 106 papers that have together received 942 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (56 papers), Topic Modeling (44 papers), Biomedical Text Mining and Ontologies (29 papers), Semantic Web and Ontologies (25 papers), Web Data Mining and Analysis (7 papers), Handwritten Text Recognition Techniques (7 papers), Speech and dialogue systems (6 papers) and Bioinformatics and Genomic Networks (6 papers). The work is most often cited by research in Artificial Intelligence (672 citations), Health Informatics (9 citations), Computer Vision and Pattern Recognition (128 citations), Molecular Biology (321 citations) and Information Systems (80 citations). Clematide has collaborated with scholars based in Switzerland, France and Germany. Frequent co-authors include Peter Makarov, Fabio Rinaldi, Martin Volk, Maud Ehrmann, Manfred Klenner, Gerold Schneider, Martin Romacker, Matteo Romanello, Kaarel Kaljurand and Christoph Kiefer. Their work appears in journals such as Database, Language Resources and Evaluation, BMC Bioinformatics, Journal of Biomedical Semantics and European Sociological Review.
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.