Jörg-Uwe Kietz
- Artificial Intelligence top 5%
- Information Systems top 5%
- Computational Theory and Mathematics top 10%
- Molecular Biology
- Computer Networks and Communications
- Co-authors
- Alexander MaedcheRaphael VolzKatharina MorikAbraham BernsteinSašo DžeroskiStefan WrobelJoaquin VanschorenWerner Emde
- Topics
- Semantic Web and Ontologies (9 papers)Advanced Database Systems and Queries (7 papers)Data Mining Algorithms and Applications (6 papers)
- Journals
- ACM Computing SurveysMachine LearningVery Large Data Bases
- Partner nations
- SwitzerlandGermanyBelgium
In The Last Decade
Jörg-Uwe Kietz
16 papers receiving 317 citations
Peers
Comparison fields: 5 of 45
- Artificial Intelligence 310
- Information Systems 149
- Computational Theory and Mathematics 58
- Molecular Biology 57
- Computer Networks and Communications 49
Countries citing papers authored by Jörg-Uwe Kietz
This map shows the geographic impact of Jörg-Uwe Kietz'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 Jörg-Uwe Kietz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jörg-Uwe Kietz more than expected).
Fields of papers citing papers by Jörg-Uwe Kietz
This network shows the impact of papers produced by Jörg-Uwe Kietz. 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 Jörg-Uwe Kietz. The network helps show where Jörg-Uwe Kietz may publish in the future.
Co-authorship network of co-authors of Jörg-Uwe Kietz
This figure shows the co-authorship network connecting the top 25 collaborators of Jörg-Uwe Kietz. A scholar is included among the top collaborators of Jörg-Uwe Kietz 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 Jörg-Uwe Kietz. Jörg-Uwe Kietz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 52 | |
| 3 | 8 | |
| 4 | 4 | |
| 5 | 20 | |
| 6 | Ontology Learning from Text: Tasks and Challenges for Machine Learning. | 1 |
| 7 | 1 | |
| 8 | 1 | |
| 9 | A Method for Semi-Automatic Ontology Acquisition from a Corporate Intranet | 95 |
| 10 | 38 | |
| 11 | A data mining support environment and its application on insurance data | 3 |
| 12 | ADLER: An Environment for Mining Insurance Data. | 2 |
| 13 | Mining Insurance Data at Swiss Life | 4 |
| 14 | 52 | |
| 15 | 32 | |
| 16 | Knowledge Acquisition and Machine Learning: Theory, Methods, and Applications | 50 |
About Jörg-Uwe Kietz
Jörg-Uwe Kietz is a scholar working on Information Systems, Artificial Intelligence and Management Information Systems, having authored 16 papers that have together received 365 indexed citations. Recurring topics across this work include Semantic Web and Ontologies (9 papers), Advanced Database Systems and Queries (7 papers) and Data Mining Algorithms and Applications (6 papers). The work is most often cited by research in Artificial Intelligence (310 citations), Information Systems (149 citations) and Computational Theory and Mathematics (58 citations). Jörg-Uwe Kietz has collaborated with scholars based in Switzerland, Germany and Belgium. Frequent co-authors include Alexander Maedche, Raphael Volz, Katharina Morik, Abraham Bernstein, Sašo Džeroski, Stefan Wrobel, Joaquin Vanschoren, Werner Emde, Simon Fischer and Ulrich Reimer. Their work appears in journals such as ACM Computing Surveys, Machine Learning and Very Large Data Bases.
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.